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1475bb05
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PaddleDetection
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1475bb05
编写于
2月 06, 2020
作者:
K
Kaipeng Deng
提交者:
GitHub
2月 06, 2020
浏览文件
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差异文件
exit 1 when travis check failed (#214)
* exit1 in travis failed
上级
fcfdbd2e
变更
16
显示空白变更内容
内联
并排
Showing
16 changed file
with
982 addition
and
873 deletion
+982
-873
.travis.yml
.travis.yml
+1
-0
.travis/requirements.txt
.travis/requirements.txt
+3
-0
.travis/unittest.sh
.travis/unittest.sh
+6
-3
inference/tools/detection_result_pb2.py
inference/tools/detection_result_pb2.py
+14
-14
inference/tools/vis.py
inference/tools/vis.py
+4
-4
ppdet/core/config/schema.py
ppdet/core/config/schema.py
+5
-0
ppdet/data/tools/x2coco.py
ppdet/data/tools/x2coco.py
+3
-5
ppdet/modeling/backbones/cb_resnet.py
ppdet/modeling/backbones/cb_resnet.py
+51
-40
ppdet/modeling/backbones/hrfpn.py
ppdet/modeling/backbones/hrfpn.py
+44
-38
ppdet/modeling/backbones/hrnet.py
ppdet/modeling/backbones/hrnet.py
+196
-142
ppdet/modeling/backbones/res2net.py
ppdet/modeling/backbones/res2net.py
+77
-71
ppdet/modeling/losses/iou_loss.py
ppdet/modeling/losses/iou_loss.py
+27
-16
ppdet/modeling/losses/yolo_loss.py
ppdet/modeling/losses/yolo_loss.py
+2
-2
ppdet/modeling/roi_heads/cascade_head.py
ppdet/modeling/roi_heads/cascade_head.py
+18
-14
ppdet/utils/oid_eval.py
ppdet/utils/oid_eval.py
+504
-505
slim/sensitive/sensitive.py
slim/sensitive/sensitive.py
+27
-19
未找到文件。
.travis.yml
浏览文件 @
1475bb05
...
@@ -26,6 +26,7 @@ script:
...
@@ -26,6 +26,7 @@ script:
-
.travis/precommit.sh || exit_code=$(( exit_code | $? ))
-
.travis/precommit.sh || exit_code=$(( exit_code | $? ))
-
docker run -i --rm -v "$PWD:/py_unittest" paddlepaddle/paddle:latest /bin/bash -c
-
docker run -i --rm -v "$PWD:/py_unittest" paddlepaddle/paddle:latest /bin/bash -c
'cd /py_unittest; sh .travis/unittest.sh' || exit_code=$(( exit_code | $? ))
'cd /py_unittest; sh .travis/unittest.sh' || exit_code=$(( exit_code | $? ))
-
if [ $exit_code -eq 0 ]; then
true
; else exit 1; fi;
notifications
:
notifications
:
email
:
email
:
...
...
.travis/requirements.txt
0 → 100644
浏览文件 @
1475bb05
# add python requirements for unittests here, note install Cython
# and pycocotools directly is not supported in travis ci.
tqdm
.travis/unittest.sh
浏览文件 @
1475bb05
...
@@ -3,6 +3,8 @@
...
@@ -3,6 +3,8 @@
abort
(){
abort
(){
echo
"Run unittest failed"
1>&2
echo
"Run unittest failed"
1>&2
echo
"Please check your code"
1>&2
echo
"Please check your code"
1>&2
echo
" 1. you can run unit tests by 'bash .travis/unittest.sh' locally"
1>&2
echo
" 2. you can add python requirements in .travis/requirements.txt if you use new requirements in unit tests"
1>&2
exit
1
exit
1
}
}
...
@@ -18,10 +20,11 @@ unittest(){
...
@@ -18,10 +20,11 @@ unittest(){
trap
'abort'
0
trap
'abort'
0
set
-e
set
-e
# install python dependencies
# install
travis
python dependencies
if
[
-f
"requirements.txt"
]
;
then
if
[
-f
"
.travis/
requirements.txt"
]
;
then
pip
install
-r
requirements.txt
pip
install
-r
.travis/
requirements.txt
fi
fi
export
PYTHONPATH
=
`
pwd
`
:
$PYTHONPATH
export
PYTHONPATH
=
`
pwd
`
:
$PYTHONPATH
unittest
.
unittest
.
...
...
inference/tools/detection_result_pb2.py
浏览文件 @
1475bb05
...
@@ -134,8 +134,7 @@ _DETECTIONBOX = _descriptor.Descriptor(
...
@@ -134,8 +134,7 @@ _DETECTIONBOX = _descriptor.Descriptor(
extension_ranges
=
[],
extension_ranges
=
[],
oneofs
=
[],
oneofs
=
[],
serialized_start
=
43
,
serialized_start
=
43
,
serialized_end
=
175
,
serialized_end
=
175
)
)
_DETECTIONRESULT
=
_descriptor
.
Descriptor
(
_DETECTIONRESULT
=
_descriptor
.
Descriptor
(
name
=
'DetectionResult'
,
name
=
'DetectionResult'
,
...
@@ -186,8 +185,7 @@ _DETECTIONRESULT = _descriptor.Descriptor(
...
@@ -186,8 +185,7 @@ _DETECTIONRESULT = _descriptor.Descriptor(
extension_ranges
=
[],
extension_ranges
=
[],
oneofs
=
[],
oneofs
=
[],
serialized_start
=
177
,
serialized_start
=
177
,
serialized_end
=
267
,
serialized_end
=
267
)
)
_DETECTIONRESULT
.
fields_by_name
[
'detection_boxes'
].
message_type
=
_DETECTIONBOX
_DETECTIONRESULT
.
fields_by_name
[
'detection_boxes'
].
message_type
=
_DETECTIONBOX
DESCRIPTOR
.
message_types_by_name
[
'DetectionBox'
]
=
_DETECTIONBOX
DESCRIPTOR
.
message_types_by_name
[
'DetectionBox'
]
=
_DETECTIONBOX
...
@@ -195,8 +193,9 @@ DESCRIPTOR.message_types_by_name['DetectionResult'] = _DETECTIONRESULT
...
@@ -195,8 +193,9 @@ DESCRIPTOR.message_types_by_name['DetectionResult'] = _DETECTIONRESULT
DetectionBox
=
_reflection
.
GeneratedProtocolMessageType
(
DetectionBox
=
_reflection
.
GeneratedProtocolMessageType
(
'DetectionBox'
,
'DetectionBox'
,
(
_message
.
Message
,),
(
_message
.
Message
,
),
dict
(
DESCRIPTOR
=
_DETECTIONBOX
,
dict
(
DESCRIPTOR
=
_DETECTIONBOX
,
__module__
=
'detection_result_pb2'
__module__
=
'detection_result_pb2'
# @@protoc_insertion_point(class_scope:PaddleSolution.DetectionBox)
# @@protoc_insertion_point(class_scope:PaddleSolution.DetectionBox)
))
))
...
@@ -204,8 +203,9 @@ _sym_db.RegisterMessage(DetectionBox)
...
@@ -204,8 +203,9 @@ _sym_db.RegisterMessage(DetectionBox)
DetectionResult
=
_reflection
.
GeneratedProtocolMessageType
(
DetectionResult
=
_reflection
.
GeneratedProtocolMessageType
(
'DetectionResult'
,
'DetectionResult'
,
(
_message
.
Message
,),
(
_message
.
Message
,
),
dict
(
DESCRIPTOR
=
_DETECTIONRESULT
,
dict
(
DESCRIPTOR
=
_DETECTIONRESULT
,
__module__
=
'detection_result_pb2'
__module__
=
'detection_result_pb2'
# @@protoc_insertion_point(class_scope:PaddleSolution.DetectionResult)
# @@protoc_insertion_point(class_scope:PaddleSolution.DetectionResult)
))
))
...
...
inference/tools/vis.py
浏览文件 @
1475bb05
...
@@ -85,8 +85,8 @@ if __name__ == "__main__":
...
@@ -85,8 +85,8 @@ if __name__ == "__main__":
for
box
in
detection_result
.
detection_boxes
:
for
box
in
detection_result
.
detection_boxes
:
if
box
.
score
>=
Flags
.
threshold
:
if
box
.
score
>=
Flags
.
threshold
:
box_class
=
getattr
(
box
,
'class'
)
box_class
=
getattr
(
box
,
'class'
)
text_class_score_str
=
"%s %.2f"
%
(
class2LabelMap
.
get
(
text_class_score_str
=
"%s %.2f"
%
(
str
(
box_class
)),
box
.
score
)
class2LabelMap
.
get
(
str
(
box_class
)),
box
.
score
)
text_point
=
(
int
(
box
.
left_top_x
),
int
(
box
.
left_top_y
))
text_point
=
(
int
(
box
.
left_top_x
),
int
(
box
.
left_top_y
))
ptLeftTop
=
(
int
(
box
.
left_top_x
),
int
(
box
.
left_top_y
))
ptLeftTop
=
(
int
(
box
.
left_top_x
),
int
(
box
.
left_top_y
))
...
@@ -106,8 +106,8 @@ if __name__ == "__main__":
...
@@ -106,8 +106,8 @@ if __name__ == "__main__":
text_box_left_top
=
(
text_point
[
0
],
text_box_left_top
=
(
text_point
[
0
],
text_point
[
1
]
-
text_size
[
0
][
1
])
text_point
[
1
]
-
text_size
[
0
][
1
])
text_box_right_bottom
=
(
text_point
[
0
]
+
text_box_right_bottom
=
(
text_size
[
0
][
0
],
text_point
[
1
])
text_point
[
0
]
+
text_size
[
0
][
0
],
text_point
[
1
])
cv2
.
rectangle
(
img
,
text_box_left_top
,
cv2
.
rectangle
(
img
,
text_box_left_top
,
text_box_right_bottom
,
color
,
-
1
,
8
)
text_box_right_bottom
,
color
,
-
1
,
8
)
...
...
ppdet/core/config/schema.py
浏览文件 @
1475bb05
...
@@ -23,14 +23,19 @@ import re
...
@@ -23,14 +23,19 @@ import re
try
:
try
:
from
docstring_parser
import
parse
as
doc_parse
from
docstring_parser
import
parse
as
doc_parse
except
Exception
:
except
Exception
:
def
doc_parse
(
*
args
):
def
doc_parse
(
*
args
):
pass
pass
try
:
try
:
from
typeguard
import
check_type
from
typeguard
import
check_type
except
Exception
:
except
Exception
:
def
check_type
(
*
args
):
def
check_type
(
*
args
):
pass
pass
__all__
=
[
'SchemaValue'
,
'SchemaDict'
,
'SharedConfig'
,
'extract_schema'
]
__all__
=
[
'SchemaValue'
,
'SchemaDict'
,
'SharedConfig'
,
'extract_schema'
]
...
...
ppdet/data/tools/x2coco.py
浏览文件 @
1475bb05
...
@@ -25,11 +25,11 @@ import shutil
...
@@ -25,11 +25,11 @@ import shutil
import
numpy
as
np
import
numpy
as
np
import
PIL.ImageDraw
import
PIL.ImageDraw
label_to_num
=
{}
label_to_num
=
{}
categories_list
=
[]
categories_list
=
[]
labels_list
=
[]
labels_list
=
[]
class
MyEncoder
(
json
.
JSONEncoder
):
class
MyEncoder
(
json
.
JSONEncoder
):
def
default
(
self
,
obj
):
def
default
(
self
,
obj
):
if
isinstance
(
obj
,
np
.
integer
):
if
isinstance
(
obj
,
np
.
integer
):
...
@@ -287,16 +287,14 @@ def main():
...
@@ -287,16 +287,14 @@ def main():
indent
=
4
,
indent
=
4
,
cls
=
MyEncoder
)
cls
=
MyEncoder
)
if
args
.
val_proportion
!=
0
:
if
args
.
val_proportion
!=
0
:
val_data_coco
=
deal_json
(
args
.
dataset_type
,
val_data_coco
=
deal_json
(
args
.
dataset_type
,
args
.
output_dir
+
'/val'
,
args
.
output_dir
+
'/val'
,
args
.
json_input_dir
)
args
.
json_input_dir
)
val_json_path
=
osp
.
join
(
args
.
output_dir
+
'/annotations'
,
val_json_path
=
osp
.
join
(
args
.
output_dir
+
'/annotations'
,
'instance_val.json'
)
'instance_val.json'
)
json
.
dump
(
json
.
dump
(
val_data_coco
,
open
(
val_json_path
,
'w'
),
indent
=
4
,
cls
=
MyEncoder
)
val_data_coco
,
open
(
val_json_path
,
'w'
),
indent
=
4
,
cls
=
MyEncoder
)
if
args
.
test_proportion
!=
0
:
if
args
.
test_proportion
!=
0
:
test_data_coco
=
deal_json
(
args
.
dataset_type
,
test_data_coco
=
deal_json
(
args
.
dataset_type
,
args
.
output_dir
+
'/test'
,
args
.
output_dir
+
'/test'
,
args
.
json_input_dir
)
args
.
json_input_dir
)
test_json_path
=
osp
.
join
(
args
.
output_dir
+
'/annotations'
,
test_json_path
=
osp
.
join
(
args
.
output_dir
+
'/annotations'
,
'instance_test.json'
)
'instance_test.json'
)
...
...
ppdet/modeling/backbones/cb_resnet.py
浏览文件 @
1475bb05
...
@@ -64,8 +64,8 @@ class CBResNet(object):
...
@@ -64,8 +64,8 @@ class CBResNet(object):
variant
=
'b'
,
variant
=
'b'
,
feature_maps
=
[
2
,
3
,
4
,
5
],
feature_maps
=
[
2
,
3
,
4
,
5
],
dcn_v2_stages
=
[],
dcn_v2_stages
=
[],
nonlocal_stages
=
[],
nonlocal_stages
=
[],
repeat_num
=
2
):
repeat_num
=
2
):
super
(
CBResNet
,
self
).
__init__
()
super
(
CBResNet
,
self
).
__init__
()
if
isinstance
(
feature_maps
,
Integral
):
if
isinstance
(
feature_maps
,
Integral
):
...
@@ -102,19 +102,26 @@ class CBResNet(object):
...
@@ -102,19 +102,26 @@ class CBResNet(object):
self
.
nonlocal_stages
=
nonlocal_stages
self
.
nonlocal_stages
=
nonlocal_stages
self
.
nonlocal_mod_cfg
=
{
self
.
nonlocal_mod_cfg
=
{
50
:
2
,
50
:
2
,
101
:
5
,
101
:
5
,
152
:
8
,
152
:
8
,
200
:
12
,
200
:
12
,
}
}
self
.
stage_filters
=
[
64
,
128
,
256
,
512
]
self
.
stage_filters
=
[
64
,
128
,
256
,
512
]
self
.
_c1_out_chan_num
=
64
self
.
_c1_out_chan_num
=
64
self
.
na
=
NameAdapter
(
self
)
self
.
na
=
NameAdapter
(
self
)
def
_conv_offset
(
self
,
input
,
filter_size
,
stride
,
padding
,
act
=
None
,
name
=
None
):
def
_conv_offset
(
self
,
input
,
filter_size
,
stride
,
padding
,
act
=
None
,
name
=
None
):
out_channel
=
filter_size
*
filter_size
*
3
out_channel
=
filter_size
*
filter_size
*
3
out
=
fluid
.
layers
.
conv2d
(
input
,
out
=
fluid
.
layers
.
conv2d
(
input
,
num_filters
=
out_channel
,
num_filters
=
out_channel
,
filter_size
=
filter_size
,
filter_size
=
filter_size
,
stride
=
stride
,
stride
=
stride
,
...
@@ -145,7 +152,8 @@ class CBResNet(object):
...
@@ -145,7 +152,8 @@ class CBResNet(object):
padding
=
(
filter_size
-
1
)
//
2
,
padding
=
(
filter_size
-
1
)
//
2
,
groups
=
groups
,
groups
=
groups
,
act
=
None
,
act
=
None
,
param_attr
=
ParamAttr
(
name
=
name
+
"_weights_"
+
str
(
self
.
curr_level
)),
param_attr
=
ParamAttr
(
name
=
name
+
"_weights_"
+
str
(
self
.
curr_level
)),
bias_attr
=
False
)
bias_attr
=
False
)
else
:
else
:
offset_mask
=
self
.
_conv_offset
(
offset_mask
=
self
.
_conv_offset
(
...
@@ -155,8 +163,8 @@ class CBResNet(object):
...
@@ -155,8 +163,8 @@ class CBResNet(object):
padding
=
(
filter_size
-
1
)
//
2
,
padding
=
(
filter_size
-
1
)
//
2
,
act
=
None
,
act
=
None
,
name
=
name
+
"_conv_offset_"
+
str
(
self
.
curr_level
))
name
=
name
+
"_conv_offset_"
+
str
(
self
.
curr_level
))
offset_channel
=
filter_size
**
2
*
2
offset_channel
=
filter_size
**
2
*
2
mask_channel
=
filter_size
**
2
mask_channel
=
filter_size
**
2
offset
,
mask
=
fluid
.
layers
.
split
(
offset
,
mask
=
fluid
.
layers
.
split
(
input
=
offset_mask
,
input
=
offset_mask
,
num_or_sections
=
[
offset_channel
,
mask_channel
],
num_or_sections
=
[
offset_channel
,
mask_channel
],
...
@@ -173,7 +181,8 @@ class CBResNet(object):
...
@@ -173,7 +181,8 @@ class CBResNet(object):
groups
=
groups
,
groups
=
groups
,
deformable_groups
=
1
,
deformable_groups
=
1
,
im2col_step
=
1
,
im2col_step
=
1
,
param_attr
=
ParamAttr
(
name
=
name
+
"_weights_"
+
str
(
self
.
curr_level
)),
param_attr
=
ParamAttr
(
name
=
name
+
"_weights_"
+
str
(
self
.
curr_level
)),
bias_attr
=
False
)
bias_attr
=
False
)
bn_name
=
self
.
na
.
fix_conv_norm_name
(
name
)
bn_name
=
self
.
na
.
fix_conv_norm_name
(
name
)
...
@@ -181,11 +190,11 @@ class CBResNet(object):
...
@@ -181,11 +190,11 @@ class CBResNet(object):
norm_lr
=
0.
if
self
.
freeze_norm
else
1.
norm_lr
=
0.
if
self
.
freeze_norm
else
1.
norm_decay
=
self
.
norm_decay
norm_decay
=
self
.
norm_decay
pattr
=
ParamAttr
(
pattr
=
ParamAttr
(
name
=
bn_name
+
'_scale_'
+
str
(
self
.
curr_level
),
name
=
bn_name
+
'_scale_'
+
str
(
self
.
curr_level
),
learning_rate
=
norm_lr
,
learning_rate
=
norm_lr
,
regularizer
=
L2Decay
(
norm_decay
))
regularizer
=
L2Decay
(
norm_decay
))
battr
=
ParamAttr
(
battr
=
ParamAttr
(
name
=
bn_name
+
'_offset_'
+
str
(
self
.
curr_level
),
name
=
bn_name
+
'_offset_'
+
str
(
self
.
curr_level
),
learning_rate
=
norm_lr
,
learning_rate
=
norm_lr
,
regularizer
=
L2Decay
(
norm_decay
))
regularizer
=
L2Decay
(
norm_decay
))
...
@@ -194,11 +203,12 @@ class CBResNet(object):
...
@@ -194,11 +203,12 @@ class CBResNet(object):
out
=
fluid
.
layers
.
batch_norm
(
out
=
fluid
.
layers
.
batch_norm
(
input
=
conv
,
input
=
conv
,
act
=
act
,
act
=
act
,
name
=
bn_name
+
'.output.1_'
+
str
(
self
.
curr_level
),
name
=
bn_name
+
'.output.1_'
+
str
(
self
.
curr_level
),
param_attr
=
pattr
,
param_attr
=
pattr
,
bias_attr
=
battr
,
bias_attr
=
battr
,
moving_mean_name
=
bn_name
+
'_mean_'
+
str
(
self
.
curr_level
),
moving_mean_name
=
bn_name
+
'_mean_'
+
str
(
self
.
curr_level
),
moving_variance_name
=
bn_name
+
'_variance_'
+
str
(
self
.
curr_level
),
moving_variance_name
=
bn_name
+
'_variance_'
+
str
(
self
.
curr_level
),
use_global_stats
=
global_stats
)
use_global_stats
=
global_stats
)
scale
=
fluid
.
framework
.
_get_var
(
pattr
.
name
)
scale
=
fluid
.
framework
.
_get_var
(
pattr
.
name
)
bias
=
fluid
.
framework
.
_get_var
(
battr
.
name
)
bias
=
fluid
.
framework
.
_get_var
(
battr
.
name
)
...
@@ -262,7 +272,7 @@ class CBResNet(object):
...
@@ -262,7 +272,7 @@ class CBResNet(object):
act
=
act
,
act
=
act
,
groups
=
g
,
groups
=
g
,
name
=
_name
,
name
=
_name
,
dcn
=
(
i
==
1
and
dcn
))
dcn
=
(
i
==
1
and
dcn
))
short
=
self
.
_shortcut
(
short
=
self
.
_shortcut
(
input
,
input
,
num_filters
*
expand
,
num_filters
*
expand
,
...
@@ -273,8 +283,7 @@ class CBResNet(object):
...
@@ -273,8 +283,7 @@ class CBResNet(object):
if
callable
(
getattr
(
self
,
'_squeeze_excitation'
,
None
)):
if
callable
(
getattr
(
self
,
'_squeeze_excitation'
,
None
)):
residual
=
self
.
_squeeze_excitation
(
residual
=
self
.
_squeeze_excitation
(
input
=
residual
,
num_channels
=
num_filters
,
name
=
'fc'
+
name
)
input
=
residual
,
num_channels
=
num_filters
,
name
=
'fc'
+
name
)
return
fluid
.
layers
.
elementwise_add
(
return
fluid
.
layers
.
elementwise_add
(
x
=
short
,
y
=
residual
,
act
=
'relu'
)
x
=
short
,
y
=
residual
,
act
=
'relu'
)
def
basicblock
(
self
,
input
,
num_filters
,
stride
,
is_first
,
name
,
dcn
=
False
):
def
basicblock
(
self
,
input
,
num_filters
,
stride
,
is_first
,
name
,
dcn
=
False
):
assert
dcn
is
False
,
"Not implemented yet."
assert
dcn
is
False
,
"Not implemented yet."
...
@@ -313,10 +322,10 @@ class CBResNet(object):
...
@@ -313,10 +322,10 @@ class CBResNet(object):
is_first
=
False
if
stage_num
!=
2
else
True
is_first
=
False
if
stage_num
!=
2
else
True
dcn
=
True
if
stage_num
in
self
.
dcn_v2_stages
else
False
dcn
=
True
if
stage_num
in
self
.
dcn_v2_stages
else
False
nonlocal_mod
=
1000
nonlocal_mod
=
1000
if
stage_num
in
self
.
nonlocal_stages
:
if
stage_num
in
self
.
nonlocal_stages
:
nonlocal_mod
=
self
.
nonlocal_mod_cfg
[
self
.
depth
]
if
stage_num
==
4
else
2
nonlocal_mod
=
self
.
nonlocal_mod_cfg
[
self
.
depth
]
if
stage_num
==
4
else
2
# Make the layer name and parameter name consistent
# Make the layer name and parameter name consistent
# with ImageNet pre-trained model
# with ImageNet pre-trained model
...
@@ -335,11 +344,12 @@ class CBResNet(object):
...
@@ -335,11 +344,12 @@ class CBResNet(object):
# add non local model
# add non local model
dim_in
=
conv
.
shape
[
1
]
dim_in
=
conv
.
shape
[
1
]
nonlocal_name
=
"nonlocal_conv{}_lvl{}"
.
format
(
stage_num
,
self
.
curr_level
)
nonlocal_name
=
"nonlocal_conv{}_lvl{}"
.
format
(
stage_num
,
self
.
curr_level
)
if
i
%
nonlocal_mod
==
nonlocal_mod
-
1
:
if
i
%
nonlocal_mod
==
nonlocal_mod
-
1
:
conv
=
add_space_nonlocal
(
conv
=
add_space_nonlocal
(
conv
,
dim_in
,
dim_in
,
conv
,
dim_in
,
dim_in
,
nonlocal_name
+
'_{}'
.
format
(
i
)
,
nonlocal_name
+
'_{}'
.
format
(
i
),
int
(
dim_in
/
2
)
)
int
(
dim_in
/
2
)
)
return
conv
return
conv
...
@@ -349,9 +359,9 @@ class CBResNet(object):
...
@@ -349,9 +359,9 @@ class CBResNet(object):
conv1_name
=
self
.
na
.
fix_c1_stage_name
()
conv1_name
=
self
.
na
.
fix_c1_stage_name
()
if
self
.
variant
in
[
'c'
,
'd'
]:
if
self
.
variant
in
[
'c'
,
'd'
]:
conv1_1_name
=
"conv1_1"
conv1_1_name
=
"conv1_1"
conv1_2_name
=
"conv1_2"
conv1_2_name
=
"conv1_2"
conv1_3_name
=
"conv1_3"
conv1_3_name
=
"conv1_3"
conv_def
=
[
conv_def
=
[
[
out_chan
//
2
,
3
,
2
,
conv1_1_name
],
[
out_chan
//
2
,
3
,
2
,
conv1_1_name
],
[
out_chan
//
2
,
3
,
1
,
conv1_2_name
],
[
out_chan
//
2
,
3
,
1
,
conv1_2_name
],
...
@@ -377,14 +387,15 @@ class CBResNet(object):
...
@@ -377,14 +387,15 @@ class CBResNet(object):
pool_type
=
'max'
)
pool_type
=
'max'
)
return
output
return
output
def
connect
(
self
,
left
,
right
,
name
):
def
connect
(
self
,
left
,
right
,
name
):
ch_right
=
right
.
shape
[
1
]
ch_right
=
right
.
shape
[
1
]
conv
=
self
.
_conv_norm
(
left
,
conv
=
self
.
_conv_norm
(
left
,
num_filters
=
ch_right
,
num_filters
=
ch_right
,
filter_size
=
1
,
filter_size
=
1
,
stride
=
1
,
stride
=
1
,
act
=
"relu"
,
act
=
"relu"
,
name
=
name
+
"_connect"
)
name
=
name
+
"_connect"
)
shape
=
fluid
.
layers
.
shape
(
right
)
shape
=
fluid
.
layers
.
shape
(
right
)
shape_hw
=
fluid
.
layers
.
slice
(
shape
,
axes
=
[
0
],
starts
=
[
2
],
ends
=
[
4
])
shape_hw
=
fluid
.
layers
.
slice
(
shape
,
axes
=
[
0
],
starts
=
[
2
],
ends
=
[
4
])
out_shape_
=
shape_hw
out_shape_
=
shape_hw
...
@@ -414,11 +425,11 @@ class CBResNet(object):
...
@@ -414,11 +425,11 @@ class CBResNet(object):
for
num
in
range
(
1
,
self
.
repeat_num
):
for
num
in
range
(
1
,
self
.
repeat_num
):
self
.
curr_level
=
num
self
.
curr_level
=
num
res
=
self
.
c1_stage
(
input
)
res
=
self
.
c1_stage
(
input
)
for
i
in
range
(
len
(
res_endpoints
)
):
for
i
in
range
(
len
(
res_endpoints
)
):
res
=
self
.
connect
(
res_endpoints
[
i
],
res
,
"test_c"
+
str
(
i
+
1
)
)
res
=
self
.
connect
(
res_endpoints
[
i
],
res
,
"test_c"
+
str
(
i
+
1
)
)
res
=
self
.
layer_warp
(
res
,
i
+
2
)
res
=
self
.
layer_warp
(
res
,
i
+
2
)
res_endpoints
[
i
]
=
res
res_endpoints
[
i
]
=
res
if
self
.
freeze_at
>=
i
+
2
:
if
self
.
freeze_at
>=
i
+
2
:
res
.
stop_gradient
=
True
res
.
stop_gradient
=
True
return
OrderedDict
([(
'res{}_sum'
.
format
(
self
.
feature_maps
[
idx
]),
feat
)
return
OrderedDict
([(
'res{}_sum'
.
format
(
self
.
feature_maps
[
idx
]),
feat
)
...
...
ppdet/modeling/backbones/hrfpn.py
浏览文件 @
1475bb05
...
@@ -40,12 +40,12 @@ class HRFPN(object):
...
@@ -40,12 +40,12 @@ class HRFPN(object):
spatial_scale (list): feature map scaling factor
spatial_scale (list): feature map scaling factor
"""
"""
def
__init__
(
self
,
def
__init__
(
self
,
num_chan
=
256
,
num_chan
=
256
,
pooling_type
=
"avg"
,
pooling_type
=
"avg"
,
share_conv
=
False
,
share_conv
=
False
,
spatial_scale
=
[
1.
/
64
,
1.
/
32
,
1.
/
16
,
1.
/
8
,
1.
/
4
],
spatial_scale
=
[
1.
/
64
,
1.
/
32
,
1.
/
16
,
1.
/
8
,
1.
/
4
],
):
):
self
.
num_chan
=
num_chan
self
.
num_chan
=
num_chan
self
.
pooling_type
=
pooling_type
self
.
pooling_type
=
pooling_type
self
.
share_conv
=
share_conv
self
.
share_conv
=
share_conv
...
@@ -63,11 +63,12 @@ class HRFPN(object):
...
@@ -63,11 +63,12 @@ class HRFPN(object):
# resize
# resize
for
i
in
range
(
1
,
len
(
body_dict
)):
for
i
in
range
(
1
,
len
(
body_dict
)):
resized
=
self
.
resize_input_tensor
(
body_dict
[
body_name_list
[
i
]],
outs
[
0
],
2
**
i
)
resized
=
self
.
resize_input_tensor
(
body_dict
[
body_name_list
[
i
]],
outs
.
append
(
resized
)
outs
[
0
],
2
**
i
)
outs
.
append
(
resized
)
# concat
# concat
out
=
fluid
.
layers
.
concat
(
outs
,
axis
=
1
)
out
=
fluid
.
layers
.
concat
(
outs
,
axis
=
1
)
# reduction
# reduction
out
=
fluid
.
layers
.
conv2d
(
out
=
fluid
.
layers
.
conv2d
(
...
@@ -82,28 +83,34 @@ class HRFPN(object):
...
@@ -82,28 +83,34 @@ class HRFPN(object):
# conv
# conv
outs
=
[
out
]
outs
=
[
out
]
for
i
in
range
(
1
,
num_out
):
for
i
in
range
(
1
,
num_out
):
outs
.
append
(
self
.
pooling
(
out
,
size
=
2
**
i
,
stride
=
2
**
i
,
pooling_type
=
self
.
pooling_type
))
outs
.
append
(
self
.
pooling
(
out
,
size
=
2
**
i
,
stride
=
2
**
i
,
pooling_type
=
self
.
pooling_type
))
outputs
=
[]
outputs
=
[]
for
i
in
range
(
num_out
):
for
i
in
range
(
num_out
):
conv_name
=
"shared_fpn_conv"
if
self
.
share_conv
else
"shared_fpn_conv_"
+
str
(
i
)
conv_name
=
"shared_fpn_conv"
if
self
.
share_conv
else
"shared_fpn_conv_"
+
str
(
i
)
conv
=
fluid
.
layers
.
conv2d
(
conv
=
fluid
.
layers
.
conv2d
(
input
=
outs
[
i
],
input
=
outs
[
i
],
num_filters
=
self
.
num_chan
,
num_filters
=
self
.
num_chan
,
filter_size
=
3
,
filter_size
=
3
,
stride
=
1
,
stride
=
1
,
padding
=
1
,
padding
=
1
,
param_attr
=
ParamAttr
(
name
=
conv_name
+
"_weights"
),
param_attr
=
ParamAttr
(
name
=
conv_name
+
"_weights"
),
bias_attr
=
False
)
bias_attr
=
False
)
outputs
.
append
(
conv
)
outputs
.
append
(
conv
)
for
idx
in
range
(
0
,
num_out
-
len
(
body_name_list
)):
for
idx
in
range
(
0
,
num_out
-
len
(
body_name_list
)):
body_name_list
.
append
(
"fpn_res5_sum_subsampled_{}x"
.
format
(
2
**
(
idx
+
1
)
))
body_name_list
.
append
(
"fpn_res5_sum_subsampled_{}x"
.
format
(
2
**
(
idx
+
1
)))
outputs
=
outputs
[::
-
1
]
outputs
=
outputs
[::
-
1
]
body_name_list
=
body_name_list
[::
-
1
]
body_name_list
=
body_name_list
[::
-
1
]
res_dict
=
OrderedDict
([(
body_name_list
[
k
],
outputs
[
k
])
for
k
in
range
(
len
(
body_name_list
))])
res_dict
=
OrderedDict
([(
body_name_list
[
k
],
outputs
[
k
])
for
k
in
range
(
len
(
body_name_list
))])
return
res_dict
,
self
.
spatial_scale
return
res_dict
,
self
.
spatial_scale
def
resize_input_tensor
(
self
,
body_input
,
ref_output
,
scale
):
def
resize_input_tensor
(
self
,
body_input
,
ref_output
,
scale
):
...
@@ -117,10 +124,9 @@ class HRFPN(object):
...
@@ -117,10 +124,9 @@ class HRFPN(object):
return
body_output
return
body_output
def
pooling
(
self
,
input
,
size
,
stride
,
pooling_type
):
def
pooling
(
self
,
input
,
size
,
stride
,
pooling_type
):
pool
=
fluid
.
layers
.
pool2d
(
input
=
input
,
pool
=
fluid
.
layers
.
pool2d
(
input
=
input
,
pool_size
=
size
,
pool_size
=
size
,
pool_stride
=
stride
,
pool_stride
=
stride
,
pool_type
=
pooling_type
)
pool_type
=
pooling_type
)
return
pool
return
pool
\ No newline at end of file
ppdet/modeling/backbones/hrnet.py
浏览文件 @
1475bb05
...
@@ -92,13 +92,15 @@ class HRNet(object):
...
@@ -92,13 +92,15 @@ class HRNet(object):
channels_2
,
channels_3
,
channels_4
=
self
.
channels
[
width
]
channels_2
,
channels_3
,
channels_4
=
self
.
channels
[
width
]
num_modules_2
,
num_modules_3
,
num_modules_4
=
1
,
4
,
3
num_modules_2
,
num_modules_3
,
num_modules_4
=
1
,
4
,
3
x
=
self
.
conv_bn_layer
(
input
=
input
,
x
=
self
.
conv_bn_layer
(
input
=
input
,
filter_size
=
3
,
filter_size
=
3
,
num_filters
=
64
,
num_filters
=
64
,
stride
=
2
,
stride
=
2
,
if_act
=
True
,
if_act
=
True
,
name
=
'layer1_1'
)
name
=
'layer1_1'
)
x
=
self
.
conv_bn_layer
(
input
=
x
,
x
=
self
.
conv_bn_layer
(
input
=
x
,
filter_size
=
3
,
filter_size
=
3
,
num_filters
=
64
,
num_filters
=
64
,
stride
=
2
,
stride
=
2
,
...
@@ -119,10 +121,11 @@ class HRNet(object):
...
@@ -119,10 +121,11 @@ class HRNet(object):
def
layer1
(
self
,
input
,
name
=
None
):
def
layer1
(
self
,
input
,
name
=
None
):
conv
=
input
conv
=
input
for
i
in
range
(
4
):
for
i
in
range
(
4
):
conv
=
self
.
bottleneck_block
(
conv
,
conv
=
self
.
bottleneck_block
(
conv
,
num_filters
=
64
,
num_filters
=
64
,
downsample
=
True
if
i
==
0
else
False
,
downsample
=
True
if
i
==
0
else
False
,
name
=
name
+
'_'
+
str
(
i
+
1
))
name
=
name
+
'_'
+
str
(
i
+
1
))
return
conv
return
conv
def
transition_layer
(
self
,
x
,
in_channels
,
out_channels
,
name
=
None
):
def
transition_layer
(
self
,
x
,
in_channels
,
out_channels
,
name
=
None
):
...
@@ -132,19 +135,21 @@ class HRNet(object):
...
@@ -132,19 +135,21 @@ class HRNet(object):
for
i
in
range
(
num_out
):
for
i
in
range
(
num_out
):
if
i
<
num_in
:
if
i
<
num_in
:
if
in_channels
[
i
]
!=
out_channels
[
i
]:
if
in_channels
[
i
]
!=
out_channels
[
i
]:
residual
=
self
.
conv_bn_layer
(
x
[
i
],
residual
=
self
.
conv_bn_layer
(
x
[
i
],
filter_size
=
3
,
filter_size
=
3
,
num_filters
=
out_channels
[
i
],
num_filters
=
out_channels
[
i
],
name
=
name
+
'_layer_'
+
str
(
i
+
1
))
name
=
name
+
'_layer_'
+
str
(
i
+
1
))
out
.
append
(
residual
)
out
.
append
(
residual
)
else
:
else
:
out
.
append
(
x
[
i
])
out
.
append
(
x
[
i
])
else
:
else
:
residual
=
self
.
conv_bn_layer
(
x
[
-
1
],
residual
=
self
.
conv_bn_layer
(
x
[
-
1
],
filter_size
=
3
,
filter_size
=
3
,
num_filters
=
out_channels
[
i
],
num_filters
=
out_channels
[
i
],
stride
=
2
,
stride
=
2
,
name
=
name
+
'_layer_'
+
str
(
i
+
1
))
name
=
name
+
'_layer_'
+
str
(
i
+
1
))
out
.
append
(
residual
)
out
.
append
(
residual
)
return
out
return
out
...
@@ -153,9 +158,11 @@ class HRNet(object):
...
@@ -153,9 +158,11 @@ class HRNet(object):
for
i
in
range
(
len
(
channels
)):
for
i
in
range
(
len
(
channels
)):
residual
=
x
[
i
]
residual
=
x
[
i
]
for
j
in
range
(
block_num
):
for
j
in
range
(
block_num
):
residual
=
self
.
basic_block
(
residual
,
residual
=
self
.
basic_block
(
residual
,
channels
[
i
],
channels
[
i
],
name
=
name
+
'_branch_layer_'
+
str
(
i
+
1
)
+
'_'
+
str
(
j
+
1
))
name
=
name
+
'_branch_layer_'
+
str
(
i
+
1
)
+
'_'
+
str
(
j
+
1
))
out
.
append
(
residual
)
out
.
append
(
residual
)
return
out
return
out
...
@@ -165,29 +172,35 @@ class HRNet(object):
...
@@ -165,29 +172,35 @@ class HRNet(object):
residual
=
x
[
i
]
residual
=
x
[
i
]
for
j
in
range
(
len
(
channels
)):
for
j
in
range
(
len
(
channels
)):
if
j
>
i
:
if
j
>
i
:
y
=
self
.
conv_bn_layer
(
x
[
j
],
y
=
self
.
conv_bn_layer
(
x
[
j
],
filter_size
=
1
,
filter_size
=
1
,
num_filters
=
channels
[
i
],
num_filters
=
channels
[
i
],
if_act
=
False
,
if_act
=
False
,
name
=
name
+
'_layer_'
+
str
(
i
+
1
)
+
'_'
+
str
(
j
+
1
))
name
=
name
+
'_layer_'
+
str
(
i
+
1
)
+
'_'
+
str
(
j
+
1
))
y
=
fluid
.
layers
.
resize_nearest
(
input
=
y
,
scale
=
2
**
(
j
-
i
))
y
=
fluid
.
layers
.
resize_nearest
(
input
=
y
,
scale
=
2
**
(
j
-
i
))
residual
=
fluid
.
layers
.
elementwise_add
(
residual
=
fluid
.
layers
.
elementwise_add
(
x
=
residual
,
y
=
y
,
act
=
None
)
x
=
residual
,
y
=
y
,
act
=
None
)
elif
j
<
i
:
elif
j
<
i
:
y
=
x
[
j
]
y
=
x
[
j
]
for
k
in
range
(
i
-
j
):
for
k
in
range
(
i
-
j
):
if
k
==
i
-
j
-
1
:
if
k
==
i
-
j
-
1
:
y
=
self
.
conv_bn_layer
(
y
,
y
=
self
.
conv_bn_layer
(
y
,
filter_size
=
3
,
filter_size
=
3
,
num_filters
=
channels
[
i
],
num_filters
=
channels
[
i
],
stride
=
2
,
if_act
=
False
,
stride
=
2
,
name
=
name
+
'_layer_'
+
str
(
i
+
1
)
+
'_'
+
str
(
j
+
1
)
+
'_'
+
str
(
k
+
1
))
if_act
=
False
,
name
=
name
+
'_layer_'
+
str
(
i
+
1
)
+
'_'
+
str
(
j
+
1
)
+
'_'
+
str
(
k
+
1
))
else
:
else
:
y
=
self
.
conv_bn_layer
(
y
,
y
=
self
.
conv_bn_layer
(
y
,
filter_size
=
3
,
filter_size
=
3
,
num_filters
=
channels
[
j
],
num_filters
=
channels
[
j
],
stride
=
2
,
stride
=
2
,
name
=
name
+
'_layer_'
+
str
(
i
+
1
)
+
'_'
+
str
(
j
+
1
)
+
'_'
+
str
(
k
+
1
))
name
=
name
+
'_layer_'
+
str
(
i
+
1
)
+
'_'
+
str
(
j
+
1
)
+
'_'
+
str
(
k
+
1
))
residual
=
fluid
.
layers
.
elementwise_add
(
residual
=
fluid
.
layers
.
elementwise_add
(
x
=
residual
,
y
=
y
,
act
=
None
)
x
=
residual
,
y
=
y
,
act
=
None
)
...
@@ -195,23 +208,36 @@ class HRNet(object):
...
@@ -195,23 +208,36 @@ class HRNet(object):
out
.
append
(
residual
)
out
.
append
(
residual
)
return
out
return
out
def
high_resolution_module
(
self
,
x
,
channels
,
multi_scale_output
=
True
,
name
=
None
):
def
high_resolution_module
(
self
,
x
,
channels
,
multi_scale_output
=
True
,
name
=
None
):
residual
=
self
.
branches
(
x
,
4
,
channels
,
name
=
name
)
residual
=
self
.
branches
(
x
,
4
,
channels
,
name
=
name
)
out
=
self
.
fuse_layers
(
residual
,
channels
,
multi_scale_output
=
multi_scale_output
,
name
=
name
)
out
=
self
.
fuse_layers
(
residual
,
channels
,
multi_scale_output
=
multi_scale_output
,
name
=
name
)
return
out
return
out
def
stage
(
self
,
x
,
num_modules
,
channels
,
multi_scale_output
=
True
,
name
=
None
):
def
stage
(
self
,
x
,
num_modules
,
channels
,
multi_scale_output
=
True
,
name
=
None
):
out
=
x
out
=
x
for
i
in
range
(
num_modules
):
for
i
in
range
(
num_modules
):
if
i
==
num_modules
-
1
and
multi_scale_output
==
False
:
if
i
==
num_modules
-
1
and
multi_scale_output
==
False
:
out
=
self
.
high_resolution_module
(
out
,
out
=
self
.
high_resolution_module
(
out
,
channels
,
channels
,
multi_scale_output
=
False
,
multi_scale_output
=
False
,
name
=
name
+
'_'
+
str
(
i
+
1
))
name
=
name
+
'_'
+
str
(
i
+
1
))
else
:
else
:
out
=
self
.
high_resolution_module
(
out
,
out
=
self
.
high_resolution_module
(
channels
,
out
,
channels
,
name
=
name
+
'_'
+
str
(
i
+
1
))
name
=
name
+
'_'
+
str
(
i
+
1
))
return
out
return
out
...
@@ -219,113 +245,142 @@ class HRNet(object):
...
@@ -219,113 +245,142 @@ class HRNet(object):
out
=
[]
out
=
[]
num_filters_list
=
[
128
,
256
,
512
,
1024
]
num_filters_list
=
[
128
,
256
,
512
,
1024
]
for
i
in
range
(
len
(
x
)):
for
i
in
range
(
len
(
x
)):
out
.
append
(
self
.
conv_bn_layer
(
input
=
x
[
i
],
out
.
append
(
self
.
conv_bn_layer
(
input
=
x
[
i
],
filter_size
=
1
,
filter_size
=
1
,
num_filters
=
num_filters_list
[
i
],
num_filters
=
num_filters_list
[
i
],
name
=
name
+
'conv_'
+
str
(
i
+
1
)))
name
=
name
+
'conv_'
+
str
(
i
+
1
)))
return
out
return
out
def
basic_block
(
self
,
def
basic_block
(
self
,
input
,
num_filters
,
stride
=
1
,
downsample
=
False
,
name
=
None
):
input
,
num_filters
,
stride
=
1
,
downsample
=
False
,
name
=
None
):
residual
=
input
residual
=
input
conv
=
self
.
conv_bn_layer
(
input
=
input
,
conv
=
self
.
conv_bn_layer
(
input
=
input
,
filter_size
=
3
,
filter_size
=
3
,
num_filters
=
num_filters
,
num_filters
=
num_filters
,
stride
=
stride
,
stride
=
stride
,
name
=
name
+
'_conv1'
)
name
=
name
+
'_conv1'
)
conv
=
self
.
conv_bn_layer
(
input
=
conv
,
conv
=
self
.
conv_bn_layer
(
input
=
conv
,
filter_size
=
3
,
filter_size
=
3
,
num_filters
=
num_filters
,
num_filters
=
num_filters
,
if_act
=
False
,
if_act
=
False
,
name
=
name
+
'_conv2'
)
name
=
name
+
'_conv2'
)
if
downsample
:
if
downsample
:
residual
=
self
.
conv_bn_layer
(
input
=
input
,
residual
=
self
.
conv_bn_layer
(
input
=
input
,
filter_size
=
1
,
filter_size
=
1
,
num_filters
=
num_filters
,
num_filters
=
num_filters
,
if_act
=
False
,
if_act
=
False
,
name
=
name
+
'_downsample'
)
name
=
name
+
'_downsample'
)
if
self
.
has_se
:
if
self
.
has_se
:
conv
=
self
.
squeeze_excitation
(
conv
=
self
.
squeeze_excitation
(
input
=
conv
,
input
=
conv
,
num_channels
=
num_filters
,
num_channels
=
num_filters
,
reduction_ratio
=
16
,
reduction_ratio
=
16
,
name
=
'fc'
+
name
)
name
=
'fc'
+
name
)
return
fluid
.
layers
.
elementwise_add
(
x
=
residual
,
y
=
conv
,
act
=
'relu'
)
return
fluid
.
layers
.
elementwise_add
(
x
=
residual
,
y
=
conv
,
act
=
'relu'
)
def
bottleneck_block
(
self
,
def
bottleneck_block
(
self
,
input
,
num_filters
,
stride
=
1
,
downsample
=
False
,
name
=
None
):
input
,
num_filters
,
stride
=
1
,
downsample
=
False
,
name
=
None
):
residual
=
input
residual
=
input
conv
=
self
.
conv_bn_layer
(
input
=
input
,
conv
=
self
.
conv_bn_layer
(
input
=
input
,
filter_size
=
1
,
filter_size
=
1
,
num_filters
=
num_filters
,
num_filters
=
num_filters
,
name
=
name
+
'_conv1'
)
name
=
name
+
'_conv1'
)
conv
=
self
.
conv_bn_layer
(
input
=
conv
,
conv
=
self
.
conv_bn_layer
(
input
=
conv
,
filter_size
=
3
,
filter_size
=
3
,
num_filters
=
num_filters
,
num_filters
=
num_filters
,
stride
=
stride
,
stride
=
stride
,
name
=
name
+
'_conv2'
)
name
=
name
+
'_conv2'
)
conv
=
self
.
conv_bn_layer
(
input
=
conv
,
conv
=
self
.
conv_bn_layer
(
input
=
conv
,
filter_size
=
1
,
filter_size
=
1
,
num_filters
=
num_filters
*
4
,
num_filters
=
num_filters
*
4
,
if_act
=
False
,
if_act
=
False
,
name
=
name
+
'_conv3'
)
name
=
name
+
'_conv3'
)
if
downsample
:
if
downsample
:
residual
=
self
.
conv_bn_layer
(
input
=
input
,
residual
=
self
.
conv_bn_layer
(
input
=
input
,
filter_size
=
1
,
filter_size
=
1
,
num_filters
=
num_filters
*
4
,
num_filters
=
num_filters
*
4
,
if_act
=
False
,
if_act
=
False
,
name
=
name
+
'_downsample'
)
name
=
name
+
'_downsample'
)
if
self
.
has_se
:
if
self
.
has_se
:
conv
=
self
.
squeeze_excitation
(
conv
=
self
.
squeeze_excitation
(
input
=
conv
,
input
=
conv
,
num_channels
=
num_filters
*
4
,
num_channels
=
num_filters
*
4
,
reduction_ratio
=
16
,
reduction_ratio
=
16
,
name
=
'fc'
+
name
)
name
=
'fc'
+
name
)
return
fluid
.
layers
.
elementwise_add
(
x
=
residual
,
y
=
conv
,
act
=
'relu'
)
return
fluid
.
layers
.
elementwise_add
(
x
=
residual
,
y
=
conv
,
act
=
'relu'
)
def
squeeze_excitation
(
self
,
input
,
num_channels
,
reduction_ratio
,
name
=
None
):
def
squeeze_excitation
(
self
,
input
,
num_channels
,
reduction_ratio
,
name
=
None
):
pool
=
fluid
.
layers
.
pool2d
(
pool
=
fluid
.
layers
.
pool2d
(
input
=
input
,
pool_size
=
0
,
pool_type
=
'avg'
,
global_pooling
=
True
)
input
=
input
,
pool_size
=
0
,
pool_type
=
'avg'
,
global_pooling
=
True
)
stdv
=
1.0
/
math
.
sqrt
(
pool
.
shape
[
1
]
*
1.0
)
stdv
=
1.0
/
math
.
sqrt
(
pool
.
shape
[
1
]
*
1.0
)
squeeze
=
fluid
.
layers
.
fc
(
input
=
pool
,
squeeze
=
fluid
.
layers
.
fc
(
input
=
pool
,
size
=
num_channels
/
reduction_ratio
,
size
=
num_channels
/
reduction_ratio
,
act
=
'relu'
,
act
=
'relu'
,
param_attr
=
fluid
.
param_attr
.
ParamAttr
(
param_attr
=
fluid
.
param_attr
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Uniform
(
initializer
=
fluid
.
initializer
.
Uniform
(
-
stdv
,
stdv
),
-
stdv
,
stdv
),
name
=
name
+
'_sqz_weights'
),
name
=
name
+
'_sqz_weights'
),
bias_attr
=
ParamAttr
(
name
=
name
+
'_sqz_offset'
))
bias_attr
=
ParamAttr
(
name
=
name
+
'_sqz_offset'
))
stdv
=
1.0
/
math
.
sqrt
(
squeeze
.
shape
[
1
]
*
1.0
)
stdv
=
1.0
/
math
.
sqrt
(
squeeze
.
shape
[
1
]
*
1.0
)
excitation
=
fluid
.
layers
.
fc
(
input
=
squeeze
,
excitation
=
fluid
.
layers
.
fc
(
input
=
squeeze
,
size
=
num_channels
,
size
=
num_channels
,
act
=
'sigmoid'
,
act
=
'sigmoid'
,
param_attr
=
fluid
.
param_attr
.
ParamAttr
(
param_attr
=
fluid
.
param_attr
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Uniform
(
initializer
=
fluid
.
initializer
.
Uniform
(
-
stdv
,
stdv
),
-
stdv
,
stdv
),
name
=
name
+
'_exc_weights'
),
name
=
name
+
'_exc_weights'
),
bias_attr
=
ParamAttr
(
name
=
name
+
'_exc_offset'
))
bias_attr
=
ParamAttr
(
name
=
name
+
'_exc_offset'
))
scale
=
fluid
.
layers
.
elementwise_mul
(
x
=
input
,
y
=
excitation
,
axis
=
0
)
scale
=
fluid
.
layers
.
elementwise_mul
(
x
=
input
,
y
=
excitation
,
axis
=
0
)
return
scale
return
scale
def
conv_bn_layer
(
self
,
input
,
filter_size
,
num_filters
,
stride
=
1
,
padding
=
1
,
num_groups
=
1
,
if_act
=
True
,
name
=
None
):
def
conv_bn_layer
(
self
,
input
,
filter_size
,
num_filters
,
stride
=
1
,
padding
=
1
,
num_groups
=
1
,
if_act
=
True
,
name
=
None
):
conv
=
fluid
.
layers
.
conv2d
(
conv
=
fluid
.
layers
.
conv2d
(
input
=
input
,
input
=
input
,
num_filters
=
num_filters
,
num_filters
=
num_filters
,
filter_size
=
filter_size
,
filter_size
=
filter_size
,
stride
=
stride
,
stride
=
stride
,
padding
=
(
filter_size
-
1
)
//
2
,
padding
=
(
filter_size
-
1
)
//
2
,
groups
=
num_groups
,
groups
=
num_groups
,
act
=
None
,
act
=
None
,
param_attr
=
ParamAttr
(
initializer
=
MSRA
(),
name
=
name
+
'_weights'
),
param_attr
=
ParamAttr
(
initializer
=
MSRA
(),
name
=
name
+
'_weights'
),
bias_attr
=
False
)
bias_attr
=
False
)
bn_name
=
name
+
'_bn'
bn_name
=
name
+
'_bn'
bn
=
self
.
_bn
(
input
=
conv
,
bn_name
=
bn_name
)
bn
=
self
.
_bn
(
input
=
conv
,
bn_name
=
bn_name
)
if
if_act
:
if
if_act
:
bn
=
fluid
.
layers
.
relu
(
bn
)
bn
=
fluid
.
layers
.
relu
(
bn
)
return
bn
return
bn
def
_bn
(
self
,
def
_bn
(
self
,
input
,
act
=
None
,
bn_name
=
None
):
input
,
act
=
None
,
bn_name
=
None
):
norm_lr
=
0.
if
self
.
freeze_norm
else
1.
norm_lr
=
0.
if
self
.
freeze_norm
else
1.
norm_decay
=
self
.
norm_decay
norm_decay
=
self
.
norm_decay
pattr
=
ParamAttr
(
pattr
=
ParamAttr
(
...
@@ -363,10 +418,10 @@ class HRNet(object):
...
@@ -363,10 +418,10 @@ class HRNet(object):
res
=
input
res
=
input
feature_maps
=
self
.
feature_maps
feature_maps
=
self
.
feature_maps
self
.
net
(
input
)
self
.
net
(
input
)
for
i
in
feature_maps
:
for
i
in
feature_maps
:
res
=
self
.
end_points
[
i
-
2
]
res
=
self
.
end_points
[
i
-
2
]
if
i
in
self
.
feature_maps
:
if
i
in
self
.
feature_maps
:
res_endpoints
.
append
(
res
)
res_endpoints
.
append
(
res
)
if
self
.
freeze_at
>=
i
:
if
self
.
freeze_at
>=
i
:
...
@@ -374,4 +429,3 @@ class HRNet(object):
...
@@ -374,4 +429,3 @@ class HRNet(object):
return
OrderedDict
([(
'res{}_sum'
.
format
(
self
.
feature_maps
[
idx
]),
feat
)
return
OrderedDict
([(
'res{}_sum'
.
format
(
self
.
feature_maps
[
idx
]),
feat
)
for
idx
,
feat
in
enumerate
(
res_endpoints
)])
for
idx
,
feat
in
enumerate
(
res_endpoints
)])
ppdet/modeling/backbones/res2net.py
浏览文件 @
1475bb05
...
@@ -54,7 +54,8 @@ class Res2Net(ResNet):
...
@@ -54,7 +54,8 @@ class Res2Net(ResNet):
"""
"""
__shared__
=
[
'norm_type'
,
'freeze_norm'
,
'weight_prefix_name'
]
__shared__
=
[
'norm_type'
,
'freeze_norm'
,
'weight_prefix_name'
]
def
__init__
(
self
,
def
__init__
(
self
,
depth
=
50
,
depth
=
50
,
width
=
26
,
width
=
26
,
scales
=
4
,
scales
=
4
,
...
@@ -66,8 +67,9 @@ class Res2Net(ResNet):
...
@@ -66,8 +67,9 @@ class Res2Net(ResNet):
feature_maps
=
[
2
,
3
,
4
,
5
],
feature_maps
=
[
2
,
3
,
4
,
5
],
dcn_v2_stages
=
[],
dcn_v2_stages
=
[],
weight_prefix_name
=
''
,
weight_prefix_name
=
''
,
nonlocal_stages
=
[],):
nonlocal_stages
=
[],
):
super
(
Res2Net
,
self
).
__init__
(
depth
=
depth
,
super
(
Res2Net
,
self
).
__init__
(
depth
=
depth
,
freeze_at
=
freeze_at
,
freeze_at
=
freeze_at
,
norm_type
=
norm_type
,
norm_type
=
norm_type
,
freeze_norm
=
freeze_norm
,
freeze_norm
=
freeze_norm
,
...
@@ -78,7 +80,8 @@ class Res2Net(ResNet):
...
@@ -78,7 +80,8 @@ class Res2Net(ResNet):
weight_prefix_name
=
weight_prefix_name
,
weight_prefix_name
=
weight_prefix_name
,
nonlocal_stages
=
nonlocal_stages
)
nonlocal_stages
=
nonlocal_stages
)
assert
depth
>=
50
,
"just support depth>=50 in res2net, but got depth="
.
format
(
depth
)
assert
depth
>=
50
,
"just support depth>=50 in res2net, but got depth="
.
format
(
depth
)
# res2net config
# res2net config
self
.
scales
=
scales
self
.
scales
=
scales
self
.
width
=
width
self
.
width
=
width
...
@@ -107,26 +110,32 @@ class Res2Net(ResNet):
...
@@ -107,26 +110,32 @@ class Res2Net(ResNet):
ys
=
[]
ys
=
[]
for
s
in
range
(
self
.
scales
-
1
):
for
s
in
range
(
self
.
scales
-
1
):
if
s
==
0
or
stride
==
2
:
if
s
==
0
or
stride
==
2
:
ys
.
append
(
self
.
_conv_norm
(
input
=
xs
[
s
],
ys
.
append
(
num_filters
=
num_filters1
//
self
.
scales
,
self
.
_conv_norm
(
input
=
xs
[
s
],
num_filters
=
num_filters1
//
self
.
scales
,
stride
=
stride
,
stride
=
stride
,
filter_size
=
3
,
filter_size
=
3
,
act
=
'relu'
,
act
=
'relu'
,
name
=
name
+
'_branch2b_'
+
str
(
s
+
1
),
name
=
name
+
'_branch2b_'
+
str
(
s
+
1
),
dcn_v2
=
dcn_v2
))
dcn_v2
=
dcn_v2
))
else
:
else
:
ys
.
append
(
self
.
_conv_norm
(
input
=
xs
[
s
]
+
ys
[
-
1
],
ys
.
append
(
num_filters
=
num_filters1
//
self
.
scales
,
self
.
_conv_norm
(
input
=
xs
[
s
]
+
ys
[
-
1
],
num_filters
=
num_filters1
//
self
.
scales
,
stride
=
stride
,
stride
=
stride
,
filter_size
=
3
,
filter_size
=
3
,
act
=
'relu'
,
act
=
'relu'
,
name
=
name
+
'_branch2b_'
+
str
(
s
+
1
),
name
=
name
+
'_branch2b_'
+
str
(
s
+
1
),
dcn_v2
=
dcn_v2
))
dcn_v2
=
dcn_v2
))
if
stride
==
1
:
if
stride
==
1
:
ys
.
append
(
xs
[
-
1
])
ys
.
append
(
xs
[
-
1
])
else
:
else
:
ys
.
append
(
fluid
.
layers
.
pool2d
(
input
=
xs
[
-
1
],
ys
.
append
(
fluid
.
layers
.
pool2d
(
input
=
xs
[
-
1
],
pool_size
=
3
,
pool_size
=
3
,
pool_stride
=
stride
,
pool_stride
=
stride
,
pool_padding
=
1
,
pool_padding
=
1
,
...
@@ -138,18 +147,14 @@ class Res2Net(ResNet):
...
@@ -138,18 +147,14 @@ class Res2Net(ResNet):
num_filters
=
num_filters2
,
num_filters
=
num_filters2
,
filter_size
=
1
,
filter_size
=
1
,
act
=
None
,
act
=
None
,
name
=
name
+
"_branch2c"
)
name
=
name
+
"_branch2c"
)
short
=
self
.
_shortcut
(
input
,
short
=
self
.
_shortcut
(
num_filters2
,
input
,
num_filters2
,
stride
,
is_first
,
name
=
name
+
"_branch1"
)
stride
,
is_first
,
name
=
name
+
"_branch1"
)
return
fluid
.
layers
.
elementwise_add
(
return
fluid
.
layers
.
elementwise_add
(
x
=
short
,
y
=
conv2
,
act
=
'relu'
,
name
=
name
+
".add.output.5"
)
x
=
short
,
y
=
conv2
,
act
=
'relu'
,
name
=
name
+
".add.output.5"
)
def
layer_warp
(
self
,
input
,
stage_num
):
def
layer_warp
(
self
,
input
,
stage_num
):
"""
"""
Args:
Args:
...
@@ -168,12 +173,13 @@ class Res2Net(ResNet):
...
@@ -168,12 +173,13 @@ class Res2Net(ResNet):
is_first
=
False
if
stage_num
!=
2
else
True
is_first
=
False
if
stage_num
!=
2
else
True
dcn_v2
=
True
if
stage_num
in
self
.
dcn_v2_stages
else
False
dcn_v2
=
True
if
stage_num
in
self
.
dcn_v2_stages
else
False
num_filters1
=
self
.
num_filters1
[
stage_num
-
2
]
num_filters1
=
self
.
num_filters1
[
stage_num
-
2
]
num_filters2
=
self
.
num_filters2
[
stage_num
-
2
]
num_filters2
=
self
.
num_filters2
[
stage_num
-
2
]
nonlocal_mod
=
1000
nonlocal_mod
=
1000
if
stage_num
in
self
.
nonlocal_stages
:
if
stage_num
in
self
.
nonlocal_stages
:
nonlocal_mod
=
self
.
nonlocal_mod_cfg
[
self
.
depth
]
if
stage_num
==
4
else
2
nonlocal_mod
=
self
.
nonlocal_mod_cfg
[
self
.
depth
]
if
stage_num
==
4
else
2
# Make the layer name and parameter name consistent
# Make the layer name and parameter name consistent
# with ImageNet pre-trained model
# with ImageNet pre-trained model
...
@@ -193,11 +199,11 @@ class Res2Net(ResNet):
...
@@ -193,11 +199,11 @@ class Res2Net(ResNet):
# add non local model
# add non local model
dim_in
=
conv
.
shape
[
1
]
dim_in
=
conv
.
shape
[
1
]
nonlocal_name
=
"nonlocal_conv{}"
.
format
(
stage_num
)
nonlocal_name
=
"nonlocal_conv{}"
.
format
(
stage_num
)
if
i
%
nonlocal_mod
==
nonlocal_mod
-
1
:
if
i
%
nonlocal_mod
==
nonlocal_mod
-
1
:
conv
=
add_space_nonlocal
(
conv
=
add_space_nonlocal
(
conv
,
dim_in
,
dim_in
,
conv
,
dim_in
,
dim_in
,
nonlocal_name
+
'_{}'
.
format
(
i
)
,
nonlocal_name
+
'_{}'
.
format
(
i
),
int
(
dim_in
/
2
)
)
int
(
dim_in
/
2
)
)
return
conv
return
conv
...
@@ -217,7 +223,7 @@ class Res2NetC5(Res2Net):
...
@@ -217,7 +223,7 @@ class Res2NetC5(Res2Net):
variant
=
'b'
,
variant
=
'b'
,
feature_maps
=
[
5
],
feature_maps
=
[
5
],
weight_prefix_name
=
''
):
weight_prefix_name
=
''
):
super
(
Res2NetC5
,
self
).
__init__
(
depth
,
width
,
scales
,
super
(
Res2NetC5
,
self
).
__init__
(
depth
,
width
,
scales
,
freeze_at
,
freeze_at
,
norm_type
,
freeze_norm
,
norm_type
,
freeze_norm
,
norm_decay
,
norm_decay
,
variant
,
feature_maps
)
variant
,
feature_maps
)
self
.
severed_head
=
True
self
.
severed_head
=
True
ppdet/modeling/losses/iou_loss.py
浏览文件 @
1475bb05
...
@@ -36,16 +36,25 @@ class IouLoss(object):
...
@@ -36,16 +36,25 @@ class IouLoss(object):
max_height (int): max height of input to support random shape input
max_height (int): max height of input to support random shape input
max_width (int): max width of input to support random shape input
max_width (int): max width of input to support random shape input
"""
"""
def
__init__
(
self
,
loss_weight
=
2.5
,
def
__init__
(
self
,
loss_weight
=
2.5
,
max_height
=
608
,
max_width
=
608
):
max_height
=
608
,
max_width
=
608
):
self
.
_loss_weight
=
loss_weight
self
.
_loss_weight
=
loss_weight
self
.
_MAX_HI
=
max_height
self
.
_MAX_HI
=
max_height
self
.
_MAX_WI
=
max_width
self
.
_MAX_WI
=
max_width
def
__call__
(
self
,
x
,
y
,
w
,
h
,
tx
,
ty
,
tw
,
th
,
def
__call__
(
self
,
anchors
,
downsample_ratio
,
batch_size
,
eps
=
1.e-10
):
x
,
y
,
w
,
h
,
tx
,
ty
,
tw
,
th
,
anchors
,
downsample_ratio
,
batch_size
,
eps
=
1.e-10
):
'''
'''
Args:
Args:
x | y | w | h ([Variables]): the output of yolov3 for encoded x|y|w|h
x | y | w | h ([Variables]): the output of yolov3 for encoded x|y|w|h
...
@@ -55,10 +64,10 @@ class IouLoss(object):
...
@@ -55,10 +64,10 @@ class IouLoss(object):
batch_size (int): training batch size
batch_size (int): training batch size
eps (float): the decimal to prevent the denominator eqaul zero
eps (float): the decimal to prevent the denominator eqaul zero
'''
'''
x1
,
y1
,
x2
,
y2
=
self
.
_bbox_transform
(
x
,
y
,
w
,
h
,
anchors
,
x1
,
y1
,
x2
,
y2
=
self
.
_bbox_transform
(
downsample_ratio
,
batch_size
,
False
)
x
,
y
,
w
,
h
,
anchors
,
downsample_ratio
,
batch_size
,
False
)
x1g
,
y1g
,
x2g
,
y2g
=
self
.
_bbox_transform
(
tx
,
ty
,
tw
,
th
,
x1g
,
y1g
,
x2g
,
y2g
=
self
.
_bbox_transform
(
anchors
,
downsample_ratio
,
batch_size
,
True
)
tx
,
ty
,
tw
,
th
,
anchors
,
downsample_ratio
,
batch_size
,
True
)
x2
=
fluid
.
layers
.
elementwise_max
(
x1
,
x2
)
x2
=
fluid
.
layers
.
elementwise_max
(
x1
,
x2
)
y2
=
fluid
.
layers
.
elementwise_max
(
y1
,
y2
)
y2
=
fluid
.
layers
.
elementwise_max
(
y1
,
y2
)
...
@@ -76,14 +85,16 @@ class IouLoss(object):
...
@@ -76,14 +85,16 @@ class IouLoss(object):
intsctk
=
(
xkis2
-
xkis1
)
*
(
ykis2
-
ykis1
)
intsctk
=
(
xkis2
-
xkis1
)
*
(
ykis2
-
ykis1
)
intsctk
=
intsctk
*
fluid
.
layers
.
greater_than
(
intsctk
=
intsctk
*
fluid
.
layers
.
greater_than
(
xkis2
,
xkis1
)
*
fluid
.
layers
.
greater_than
(
ykis2
,
ykis1
)
xkis2
,
xkis1
)
*
fluid
.
layers
.
greater_than
(
ykis2
,
ykis1
)
unionk
=
(
x2
-
x1
)
*
(
y2
-
y1
)
+
(
x2g
-
x1g
)
*
(
y2g
-
y1g
)
-
intsctk
+
eps
unionk
=
(
x2
-
x1
)
*
(
y2
-
y1
)
+
(
x2g
-
x1g
)
*
(
y2g
-
y1g
)
-
intsctk
+
eps
iouk
=
intsctk
/
unionk
iouk
=
intsctk
/
unionk
loss_iou
=
1.
-
iouk
*
iouk
loss_iou
=
1.
-
iouk
*
iouk
loss_iou
=
loss_iou
*
self
.
_loss_weight
loss_iou
=
loss_iou
*
self
.
_loss_weight
return
loss_iou
return
loss_iou
def
_bbox_transform
(
self
,
dcx
,
dcy
,
dw
,
dh
,
anchors
,
downsample_ratio
,
batch_size
,
is_gt
):
def
_bbox_transform
(
self
,
dcx
,
dcy
,
dw
,
dh
,
anchors
,
downsample_ratio
,
batch_size
,
is_gt
):
grid_x
=
int
(
self
.
_MAX_WI
/
downsample_ratio
)
grid_x
=
int
(
self
.
_MAX_WI
/
downsample_ratio
)
grid_y
=
int
(
self
.
_MAX_HI
/
downsample_ratio
)
grid_y
=
int
(
self
.
_MAX_HI
/
downsample_ratio
)
an_num
=
len
(
anchors
)
//
2
an_num
=
len
(
anchors
)
//
2
...
@@ -125,14 +136,16 @@ class IouLoss(object):
...
@@ -125,14 +136,16 @@ class IouLoss(object):
anchor_w_np
=
np
.
array
(
anchor_w_
)
anchor_w_np
=
np
.
array
(
anchor_w_
)
anchor_w_np
=
np
.
reshape
(
anchor_w_np
,
newshape
=
[
1
,
an_num
,
1
,
1
])
anchor_w_np
=
np
.
reshape
(
anchor_w_np
,
newshape
=
[
1
,
an_num
,
1
,
1
])
anchor_w_np
=
np
.
tile
(
anchor_w_np
,
reps
=
[
batch_size
,
1
,
grid_y
,
grid_x
])
anchor_w_np
=
np
.
tile
(
anchor_w_np
,
reps
=
[
batch_size
,
1
,
grid_y
,
grid_x
])
anchor_w_max
=
self
.
_create_tensor_from_numpy
(
anchor_w_np
.
astype
(
np
.
float32
))
anchor_w_max
=
self
.
_create_tensor_from_numpy
(
anchor_w_np
.
astype
(
np
.
float32
))
anchor_w
=
fluid
.
layers
.
crop
(
x
=
anchor_w_max
,
shape
=
dcx
)
anchor_w
=
fluid
.
layers
.
crop
(
x
=
anchor_w_max
,
shape
=
dcx
)
anchor_w
.
stop_gradient
=
True
anchor_w
.
stop_gradient
=
True
anchor_h_
=
[
anchors
[
i
]
for
i
in
range
(
0
,
len
(
anchors
))
if
i
%
2
==
1
]
anchor_h_
=
[
anchors
[
i
]
for
i
in
range
(
0
,
len
(
anchors
))
if
i
%
2
==
1
]
anchor_h_np
=
np
.
array
(
anchor_h_
)
anchor_h_np
=
np
.
array
(
anchor_h_
)
anchor_h_np
=
np
.
reshape
(
anchor_h_np
,
newshape
=
[
1
,
an_num
,
1
,
1
])
anchor_h_np
=
np
.
reshape
(
anchor_h_np
,
newshape
=
[
1
,
an_num
,
1
,
1
])
anchor_h_np
=
np
.
tile
(
anchor_h_np
,
reps
=
[
batch_size
,
1
,
grid_y
,
grid_x
])
anchor_h_np
=
np
.
tile
(
anchor_h_np
,
reps
=
[
batch_size
,
1
,
grid_y
,
grid_x
])
anchor_h_max
=
self
.
_create_tensor_from_numpy
(
anchor_h_np
.
astype
(
np
.
float32
))
anchor_h_max
=
self
.
_create_tensor_from_numpy
(
anchor_h_np
.
astype
(
np
.
float32
))
anchor_h
=
fluid
.
layers
.
crop
(
x
=
anchor_h_max
,
shape
=
dcx
)
anchor_h
=
fluid
.
layers
.
crop
(
x
=
anchor_h_max
,
shape
=
dcx
)
anchor_h
.
stop_gradient
=
True
anchor_h
.
stop_gradient
=
True
# e^tw e^th
# e^tw e^th
...
@@ -148,7 +161,6 @@ class IouLoss(object):
...
@@ -148,7 +161,6 @@ class IouLoss(object):
pw
.
stop_gradient
=
True
pw
.
stop_gradient
=
True
ph
.
stop_gradient
=
True
ph
.
stop_gradient
=
True
x1
=
cx
-
0.5
*
pw
x1
=
cx
-
0.5
*
pw
y1
=
cy
-
0.5
*
ph
y1
=
cy
-
0.5
*
ph
x2
=
cx
+
0.5
*
pw
x2
=
cx
+
0.5
*
pw
...
@@ -169,4 +181,3 @@ class IouLoss(object):
...
@@ -169,4 +181,3 @@ class IouLoss(object):
default_initializer
=
NumpyArrayInitializer
(
numpy_array
))
default_initializer
=
NumpyArrayInitializer
(
numpy_array
))
paddle_array
.
stop_gradient
=
True
paddle_array
.
stop_gradient
=
True
return
paddle_array
return
paddle_array
ppdet/modeling/losses/yolo_loss.py
浏览文件 @
1475bb05
...
@@ -131,8 +131,8 @@ class YOLOv3Loss(object):
...
@@ -131,8 +131,8 @@ class YOLOv3Loss(object):
loss_h
=
fluid
.
layers
.
abs
(
h
-
th
)
*
tscale_tobj
loss_h
=
fluid
.
layers
.
abs
(
h
-
th
)
*
tscale_tobj
loss_h
=
fluid
.
layers
.
reduce_sum
(
loss_h
,
dim
=
[
1
,
2
,
3
])
loss_h
=
fluid
.
layers
.
reduce_sum
(
loss_h
,
dim
=
[
1
,
2
,
3
])
if
self
.
_iou_loss
is
not
None
:
if
self
.
_iou_loss
is
not
None
:
loss_iou
=
self
.
_iou_loss
(
x
,
y
,
w
,
h
,
tx
,
ty
,
tw
,
th
,
loss_iou
=
self
.
_iou_loss
(
x
,
y
,
w
,
h
,
tx
,
ty
,
tw
,
th
,
anchors
,
anchors
,
downsample
,
self
.
_batch_size
)
downsample
,
self
.
_batch_size
)
loss_iou
=
loss_iou
*
tscale_tobj
loss_iou
=
loss_iou
*
tscale_tobj
loss_iou
=
fluid
.
layers
.
reduce_sum
(
loss_iou
,
dim
=
[
1
,
2
,
3
])
loss_iou
=
fluid
.
layers
.
reduce_sum
(
loss_iou
,
dim
=
[
1
,
2
,
3
])
loss_ious
.
append
(
fluid
.
layers
.
reduce_mean
(
loss_iou
))
loss_ious
.
append
(
fluid
.
layers
.
reduce_mean
(
loss_iou
))
...
...
ppdet/modeling/roi_heads/cascade_head.py
浏览文件 @
1475bb05
...
@@ -220,27 +220,31 @@ class CascadeBBoxHead(object):
...
@@ -220,27 +220,31 @@ class CascadeBBoxHead(object):
pred_result
=
self
.
nms
(
bboxes
=
box_out
,
scores
=
boxes_cls_prob_mean
)
pred_result
=
self
.
nms
(
bboxes
=
box_out
,
scores
=
boxes_cls_prob_mean
)
return
{
"bbox"
:
pred_result
}
return
{
"bbox"
:
pred_result
}
def
get_prediction_cls_aware
(
self
,
def
get_prediction_cls_aware
(
self
,
im_info
,
im_shape
,
cascade_cls_prob
,
im_info
,
cascade_decoded_box
,
cascade_bbox_reg_weights
):
im_shape
,
cascade_cls_prob
,
cascade_decoded_box
,
cascade_bbox_reg_weights
):
'''
'''
get_prediction_cls_aware: predict bbox for each class
get_prediction_cls_aware: predict bbox for each class
'''
'''
cascade_num_stage
=
3
cascade_num_stage
=
3
cascade_eval_weight
=
[
0.2
,
0.3
,
0.5
]
cascade_eval_weight
=
[
0.2
,
0.3
,
0.5
]
# merge 3 stages results
# merge 3 stages results
sum_cascade_cls_prob
=
sum
([
prob
*
cascade_eval_weight
[
idx
]
for
idx
,
prob
in
enumerate
(
cascade_cls_prob
)
])
sum_cascade_cls_prob
=
sum
([
sum_cascade_decoded_box
=
sum
([
bbox
*
cascade_eval_weight
[
idx
]
for
idx
,
bbox
in
enumerate
(
cascade_decoded_box
)
])
prob
*
cascade_eval_weight
[
idx
]
for
idx
,
prob
in
enumerate
(
cascade_cls_prob
)
])
sum_cascade_decoded_box
=
sum
([
bbox
*
cascade_eval_weight
[
idx
]
for
idx
,
bbox
in
enumerate
(
cascade_decoded_box
)
])
self
.
im_scale
=
fluid
.
layers
.
slice
(
im_info
,
[
1
],
starts
=
[
2
],
ends
=
[
3
])
self
.
im_scale
=
fluid
.
layers
.
slice
(
im_info
,
[
1
],
starts
=
[
2
],
ends
=
[
3
])
im_scale_lod
=
fluid
.
layers
.
sequence_expand
(
self
.
im_scale
,
sum_cascade_decoded_box
)
im_scale_lod
=
fluid
.
layers
.
sequence_expand
(
self
.
im_scale
,
sum_cascade_decoded_box
)
sum_cascade_decoded_box
=
sum_cascade_decoded_box
/
im_scale_lod
sum_cascade_decoded_box
=
sum_cascade_decoded_box
/
im_scale_lod
decoded_bbox
=
sum_cascade_decoded_box
decoded_bbox
=
sum_cascade_decoded_box
decoded_bbox
=
fluid
.
layers
.
reshape
(
decoded_bbox
,
shape
=
(
-
1
,
self
.
num_classes
,
4
)
)
decoded_bbox
=
fluid
.
layers
.
reshape
(
decoded_bbox
,
shape
=
(
-
1
,
self
.
num_classes
,
4
))
box_out
=
fluid
.
layers
.
box_clip
(
input
=
decoded_bbox
,
im_info
=
im_shape
)
box_out
=
fluid
.
layers
.
box_clip
(
input
=
decoded_bbox
,
im_info
=
im_shape
)
pred_result
=
self
.
nms
(
bboxes
=
box_out
,
scores
=
sum_cascade_cls_prob
)
pred_result
=
self
.
nms
(
bboxes
=
box_out
,
scores
=
sum_cascade_cls_prob
)
...
...
ppdet/utils/oid_eval.py
浏览文件 @
1475bb05
...
@@ -26,519 +26,518 @@ from .coco_eval import bbox2out
...
@@ -26,519 +26,518 @@ from .coco_eval import bbox2out
import
logging
import
logging
logger
=
logging
.
getLogger
(
__name__
)
logger
=
logging
.
getLogger
(
__name__
)
__all__
=
[
'bbox2out'
,
'get_category_info'
]
__all__
=
[
'bbox2out'
,
'get_category_info'
]
def
get_category_info
(
anno_file
=
None
,
def
get_category_info
(
anno_file
=
None
,
with_background
=
True
,
with_background
=
True
,
use_default_label
=
False
):
use_default_label
=
False
):
clsid2catid
=
{
k
:
k
for
k
in
range
(
1
,
501
)
}
clsid2catid
=
{
k
:
k
for
k
in
range
(
1
,
501
)
}
catid2name
=
{
catid2name
=
{
0
:
"background"
,
0
:
"background"
,
1
:
"Infant bed"
,
1
:
"Infant bed"
,
2
:
"Rose"
,
2
:
"Rose"
,
3
:
"Flag"
,
3
:
"Flag"
,
4
:
"Flashlight"
,
4
:
"Flashlight"
,
5
:
"Sea turtle"
,
5
:
"Sea turtle"
,
6
:
"Camera"
,
6
:
"Camera"
,
7
:
"Animal"
,
7
:
"Animal"
,
8
:
"Glove"
,
8
:
"Glove"
,
9
:
"Crocodile"
,
9
:
"Crocodile"
,
10
:
"Cattle"
,
10
:
"Cattle"
,
11
:
"House"
,
11
:
"House"
,
12
:
"Guacamole"
,
12
:
"Guacamole"
,
13
:
"Penguin"
,
13
:
"Penguin"
,
14
:
"Vehicle registration plate"
,
14
:
"Vehicle registration plate"
,
15
:
"Bench"
,
15
:
"Bench"
,
16
:
"Ladybug"
,
16
:
"Ladybug"
,
17
:
"Human nose"
,
17
:
"Human nose"
,
18
:
"Watermelon"
,
18
:
"Watermelon"
,
19
:
"Flute"
,
19
:
"Flute"
,
20
:
"Butterfly"
,
20
:
"Butterfly"
,
21
:
"Washing machine"
,
21
:
"Washing machine"
,
22
:
"Raccoon"
,
22
:
"Raccoon"
,
23
:
"Segway"
,
23
:
"Segway"
,
24
:
"Taco"
,
24
:
"Taco"
,
25
:
"Jellyfish"
,
25
:
"Jellyfish"
,
26
:
"Cake"
,
26
:
"Cake"
,
27
:
"Pen"
,
27
:
"Pen"
,
28
:
"Cannon"
,
28
:
"Cannon"
,
29
:
"Bread"
,
29
:
"Bread"
,
30
:
"Tree"
,
30
:
"Tree"
,
31
:
"Shellfish"
,
31
:
"Shellfish"
,
32
:
"Bed"
,
32
:
"Bed"
,
33
:
"Hamster"
,
33
:
"Hamster"
,
34
:
"Hat"
,
34
:
"Hat"
,
35
:
"Toaster"
,
35
:
"Toaster"
,
36
:
"Sombrero"
,
36
:
"Sombrero"
,
37
:
"Tiara"
,
37
:
"Tiara"
,
38
:
"Bowl"
,
38
:
"Bowl"
,
39
:
"Dragonfly"
,
39
:
"Dragonfly"
,
40
:
"Moths and butterflies"
,
40
:
"Moths and butterflies"
,
41
:
"Antelope"
,
41
:
"Antelope"
,
42
:
"Vegetable"
,
42
:
"Vegetable"
,
43
:
"Torch"
,
43
:
"Torch"
,
44
:
"Building"
,
44
:
"Building"
,
45
:
"Power plugs and sockets"
,
45
:
"Power plugs and sockets"
,
46
:
"Blender"
,
46
:
"Blender"
,
47
:
"Billiard table"
,
47
:
"Billiard table"
,
48
:
"Cutting board"
,
48
:
"Cutting board"
,
49
:
"Bronze sculpture"
,
49
:
"Bronze sculpture"
,
50
:
"Turtle"
,
50
:
"Turtle"
,
51
:
"Broccoli"
,
51
:
"Broccoli"
,
52
:
"Tiger"
,
52
:
"Tiger"
,
53
:
"Mirror"
,
53
:
"Mirror"
,
54
:
"Bear"
,
54
:
"Bear"
,
55
:
"Zucchini"
,
55
:
"Zucchini"
,
56
:
"Dress"
,
56
:
"Dress"
,
57
:
"Volleyball"
,
57
:
"Volleyball"
,
58
:
"Guitar"
,
58
:
"Guitar"
,
59
:
"Reptile"
,
59
:
"Reptile"
,
60
:
"Golf cart"
,
60
:
"Golf cart"
,
61
:
"Tart"
,
61
:
"Tart"
,
62
:
"Fedora"
,
62
:
"Fedora"
,
63
:
"Carnivore"
,
63
:
"Carnivore"
,
64
:
"Car"
,
64
:
"Car"
,
65
:
"Lighthouse"
,
65
:
"Lighthouse"
,
66
:
"Coffeemaker"
,
66
:
"Coffeemaker"
,
67
:
"Food processor"
,
67
:
"Food processor"
,
68
:
"Truck"
,
68
:
"Truck"
,
69
:
"Bookcase"
,
69
:
"Bookcase"
,
70
:
"Surfboard"
,
70
:
"Surfboard"
,
71
:
"Footwear"
,
71
:
"Footwear"
,
72
:
"Bench"
,
72
:
"Bench"
,
73
:
"Necklace"
,
73
:
"Necklace"
,
74
:
"Flower"
,
74
:
"Flower"
,
75
:
"Radish"
,
75
:
"Radish"
,
76
:
"Marine mammal"
,
76
:
"Marine mammal"
,
77
:
"Frying pan"
,
77
:
"Frying pan"
,
78
:
"Tap"
,
78
:
"Tap"
,
79
:
"Peach"
,
79
:
"Peach"
,
80
:
"Knife"
,
80
:
"Knife"
,
81
:
"Handbag"
,
81
:
"Handbag"
,
82
:
"Laptop"
,
82
:
"Laptop"
,
83
:
"Tent"
,
83
:
"Tent"
,
84
:
"Ambulance"
,
84
:
"Ambulance"
,
85
:
"Christmas tree"
,
85
:
"Christmas tree"
,
86
:
"Eagle"
,
86
:
"Eagle"
,
87
:
"Limousine"
,
87
:
"Limousine"
,
88
:
"Kitchen & dining room table"
,
88
:
"Kitchen & dining room table"
,
89
:
"Polar bear"
,
89
:
"Polar bear"
,
90
:
"Tower"
,
90
:
"Tower"
,
91
:
"Football"
,
91
:
"Football"
,
92
:
"Willow"
,
92
:
"Willow"
,
93
:
"Human head"
,
93
:
"Human head"
,
94
:
"Stop sign"
,
94
:
"Stop sign"
,
95
:
"Banana"
,
95
:
"Banana"
,
96
:
"Mixer"
,
96
:
"Mixer"
,
97
:
"Binoculars"
,
97
:
"Binoculars"
,
98
:
"Dessert"
,
98
:
"Dessert"
,
99
:
"Bee"
,
99
:
"Bee"
,
100
:
"Chair"
,
100
:
"Chair"
,
101
:
"Wood-burning stove"
,
101
:
"Wood-burning stove"
,
102
:
"Flowerpot"
,
102
:
"Flowerpot"
,
103
:
"Beaker"
,
103
:
"Beaker"
,
104
:
"Oyster"
,
104
:
"Oyster"
,
105
:
"Woodpecker"
,
105
:
"Woodpecker"
,
106
:
"Harp"
,
106
:
"Harp"
,
107
:
"Bathtub"
,
107
:
"Bathtub"
,
108
:
"Wall clock"
,
108
:
"Wall clock"
,
109
:
"Sports uniform"
,
109
:
"Sports uniform"
,
110
:
"Rhinoceros"
,
110
:
"Rhinoceros"
,
111
:
"Beehive"
,
111
:
"Beehive"
,
112
:
"Cupboard"
,
112
:
"Cupboard"
,
113
:
"Chicken"
,
113
:
"Chicken"
,
114
:
"Man"
,
114
:
"Man"
,
115
:
"Blue jay"
,
115
:
"Blue jay"
,
116
:
"Cucumber"
,
116
:
"Cucumber"
,
117
:
"Balloon"
,
117
:
"Balloon"
,
118
:
"Kite"
,
118
:
"Kite"
,
119
:
"Fireplace"
,
119
:
"Fireplace"
,
120
:
"Lantern"
,
120
:
"Lantern"
,
121
:
"Missile"
,
121
:
"Missile"
,
122
:
"Book"
,
122
:
"Book"
,
123
:
"Spoon"
,
123
:
"Spoon"
,
124
:
"Grapefruit"
,
124
:
"Grapefruit"
,
125
:
"Squirrel"
,
125
:
"Squirrel"
,
126
:
"Orange"
,
126
:
"Orange"
,
127
:
"Coat"
,
127
:
"Coat"
,
128
:
"Punching bag"
,
128
:
"Punching bag"
,
129
:
"Zebra"
,
129
:
"Zebra"
,
130
:
"Billboard"
,
130
:
"Billboard"
,
131
:
"Bicycle"
,
131
:
"Bicycle"
,
132
:
"Door handle"
,
132
:
"Door handle"
,
133
:
"Mechanical fan"
,
133
:
"Mechanical fan"
,
134
:
"Ring binder"
,
134
:
"Ring binder"
,
135
:
"Table"
,
135
:
"Table"
,
136
:
"Parrot"
,
136
:
"Parrot"
,
137
:
"Sock"
,
137
:
"Sock"
,
138
:
"Vase"
,
138
:
"Vase"
,
139
:
"Weapon"
,
139
:
"Weapon"
,
140
:
"Shotgun"
,
140
:
"Shotgun"
,
141
:
"Glasses"
,
141
:
"Glasses"
,
142
:
"Seahorse"
,
142
:
"Seahorse"
,
143
:
"Belt"
,
143
:
"Belt"
,
144
:
"Watercraft"
,
144
:
"Watercraft"
,
145
:
"Window"
,
145
:
"Window"
,
146
:
"Giraffe"
,
146
:
"Giraffe"
,
147
:
"Lion"
,
147
:
"Lion"
,
148
:
"Tire"
,
148
:
"Tire"
,
149
:
"Vehicle"
,
149
:
"Vehicle"
,
150
:
"Canoe"
,
150
:
"Canoe"
,
151
:
"Tie"
,
151
:
"Tie"
,
152
:
"Shelf"
,
152
:
"Shelf"
,
153
:
"Picture frame"
,
153
:
"Picture frame"
,
154
:
"Printer"
,
154
:
"Printer"
,
155
:
"Human leg"
,
155
:
"Human leg"
,
156
:
"Boat"
,
156
:
"Boat"
,
157
:
"Slow cooker"
,
157
:
"Slow cooker"
,
158
:
"Croissant"
,
158
:
"Croissant"
,
159
:
"Candle"
,
159
:
"Candle"
,
160
:
"Pancake"
,
160
:
"Pancake"
,
161
:
"Pillow"
,
161
:
"Pillow"
,
162
:
"Coin"
,
162
:
"Coin"
,
163
:
"Stretcher"
,
163
:
"Stretcher"
,
164
:
"Sandal"
,
164
:
"Sandal"
,
165
:
"Woman"
,
165
:
"Woman"
,
166
:
"Stairs"
,
166
:
"Stairs"
,
167
:
"Harpsichord"
,
167
:
"Harpsichord"
,
168
:
"Stool"
,
168
:
"Stool"
,
169
:
"Bus"
,
169
:
"Bus"
,
170
:
"Suitcase"
,
170
:
"Suitcase"
,
171
:
"Human mouth"
,
171
:
"Human mouth"
,
172
:
"Juice"
,
172
:
"Juice"
,
173
:
"Skull"
,
173
:
"Skull"
,
174
:
"Door"
,
174
:
"Door"
,
175
:
"Violin"
,
175
:
"Violin"
,
176
:
"Chopsticks"
,
176
:
"Chopsticks"
,
177
:
"Digital clock"
,
177
:
"Digital clock"
,
178
:
"Sunflower"
,
178
:
"Sunflower"
,
179
:
"Leopard"
,
179
:
"Leopard"
,
180
:
"Bell pepper"
,
180
:
"Bell pepper"
,
181
:
"Harbor seal"
,
181
:
"Harbor seal"
,
182
:
"Snake"
,
182
:
"Snake"
,
183
:
"Sewing machine"
,
183
:
"Sewing machine"
,
184
:
"Goose"
,
184
:
"Goose"
,
185
:
"Helicopter"
,
185
:
"Helicopter"
,
186
:
"Seat belt"
,
186
:
"Seat belt"
,
187
:
"Coffee cup"
,
187
:
"Coffee cup"
,
188
:
"Microwave oven"
,
188
:
"Microwave oven"
,
189
:
"Hot dog"
,
189
:
"Hot dog"
,
190
:
"Countertop"
,
190
:
"Countertop"
,
191
:
"Serving tray"
,
191
:
"Serving tray"
,
192
:
"Dog bed"
,
192
:
"Dog bed"
,
193
:
"Beer"
,
193
:
"Beer"
,
194
:
"Sunglasses"
,
194
:
"Sunglasses"
,
195
:
"Golf ball"
,
195
:
"Golf ball"
,
196
:
"Waffle"
,
196
:
"Waffle"
,
197
:
"Palm tree"
,
197
:
"Palm tree"
,
198
:
"Trumpet"
,
198
:
"Trumpet"
,
199
:
"Ruler"
,
199
:
"Ruler"
,
200
:
"Helmet"
,
200
:
"Helmet"
,
201
:
"Ladder"
,
201
:
"Ladder"
,
202
:
"Office building"
,
202
:
"Office building"
,
203
:
"Tablet computer"
,
203
:
"Tablet computer"
,
204
:
"Toilet paper"
,
204
:
"Toilet paper"
,
205
:
"Pomegranate"
,
205
:
"Pomegranate"
,
206
:
"Skirt"
,
206
:
"Skirt"
,
207
:
"Gas stove"
,
207
:
"Gas stove"
,
208
:
"Cookie"
,
208
:
"Cookie"
,
209
:
"Cart"
,
209
:
"Cart"
,
210
:
"Raven"
,
210
:
"Raven"
,
211
:
"Egg"
,
211
:
"Egg"
,
212
:
"Burrito"
,
212
:
"Burrito"
,
213
:
"Goat"
,
213
:
"Goat"
,
214
:
"Kitchen knife"
,
214
:
"Kitchen knife"
,
215
:
"Skateboard"
,
215
:
"Skateboard"
,
216
:
"Salt and pepper shakers"
,
216
:
"Salt and pepper shakers"
,
217
:
"Lynx"
,
217
:
"Lynx"
,
218
:
"Boot"
,
218
:
"Boot"
,
219
:
"Platter"
,
219
:
"Platter"
,
220
:
"Ski"
,
220
:
"Ski"
,
221
:
"Swimwear"
,
221
:
"Swimwear"
,
222
:
"Swimming pool"
,
222
:
"Swimming pool"
,
223
:
"Drinking straw"
,
223
:
"Drinking straw"
,
224
:
"Wrench"
,
224
:
"Wrench"
,
225
:
"Drum"
,
225
:
"Drum"
,
226
:
"Ant"
,
226
:
"Ant"
,
227
:
"Human ear"
,
227
:
"Human ear"
,
228
:
"Headphones"
,
228
:
"Headphones"
,
229
:
"Fountain"
,
229
:
"Fountain"
,
230
:
"Bird"
,
230
:
"Bird"
,
231
:
"Jeans"
,
231
:
"Jeans"
,
232
:
"Television"
,
232
:
"Television"
,
233
:
"Crab"
,
233
:
"Crab"
,
234
:
"Microphone"
,
234
:
"Microphone"
,
235
:
"Home appliance"
,
235
:
"Home appliance"
,
236
:
"Snowplow"
,
236
:
"Snowplow"
,
237
:
"Beetle"
,
237
:
"Beetle"
,
238
:
"Artichoke"
,
238
:
"Artichoke"
,
239
:
"Jet ski"
,
239
:
"Jet ski"
,
240
:
"Stationary bicycle"
,
240
:
"Stationary bicycle"
,
241
:
"Human hair"
,
241
:
"Human hair"
,
242
:
"Brown bear"
,
242
:
"Brown bear"
,
243
:
"Starfish"
,
243
:
"Starfish"
,
244
:
"Fork"
,
244
:
"Fork"
,
245
:
"Lobster"
,
245
:
"Lobster"
,
246
:
"Corded phone"
,
246
:
"Corded phone"
,
247
:
"Drink"
,
247
:
"Drink"
,
248
:
"Saucer"
,
248
:
"Saucer"
,
249
:
"Carrot"
,
249
:
"Carrot"
,
250
:
"Insect"
,
250
:
"Insect"
,
251
:
"Clock"
,
251
:
"Clock"
,
252
:
"Castle"
,
252
:
"Castle"
,
253
:
"Tennis racket"
,
253
:
"Tennis racket"
,
254
:
"Ceiling fan"
,
254
:
"Ceiling fan"
,
255
:
"Asparagus"
,
255
:
"Asparagus"
,
256
:
"Jaguar"
,
256
:
"Jaguar"
,
257
:
"Musical instrument"
,
257
:
"Musical instrument"
,
258
:
"Train"
,
258
:
"Train"
,
259
:
"Cat"
,
259
:
"Cat"
,
260
:
"Rifle"
,
260
:
"Rifle"
,
261
:
"Dumbbell"
,
261
:
"Dumbbell"
,
262
:
"Mobile phone"
,
262
:
"Mobile phone"
,
263
:
"Taxi"
,
263
:
"Taxi"
,
264
:
"Shower"
,
264
:
"Shower"
,
265
:
"Pitcher"
,
265
:
"Pitcher"
,
266
:
"Lemon"
,
266
:
"Lemon"
,
267
:
"Invertebrate"
,
267
:
"Invertebrate"
,
268
:
"Turkey"
,
268
:
"Turkey"
,
269
:
"High heels"
,
269
:
"High heels"
,
270
:
"Bust"
,
270
:
"Bust"
,
271
:
"Elephant"
,
271
:
"Elephant"
,
272
:
"Scarf"
,
272
:
"Scarf"
,
273
:
"Barrel"
,
273
:
"Barrel"
,
274
:
"Trombone"
,
274
:
"Trombone"
,
275
:
"Pumpkin"
,
275
:
"Pumpkin"
,
276
:
"Box"
,
276
:
"Box"
,
277
:
"Tomato"
,
277
:
"Tomato"
,
278
:
"Frog"
,
278
:
"Frog"
,
279
:
"Bidet"
,
279
:
"Bidet"
,
280
:
"Human face"
,
280
:
"Human face"
,
281
:
"Houseplant"
,
281
:
"Houseplant"
,
282
:
"Van"
,
282
:
"Van"
,
283
:
"Shark"
,
283
:
"Shark"
,
284
:
"Ice cream"
,
284
:
"Ice cream"
,
285
:
"Swim cap"
,
285
:
"Swim cap"
,
286
:
"Falcon"
,
286
:
"Falcon"
,
287
:
"Ostrich"
,
287
:
"Ostrich"
,
288
:
"Handgun"
,
288
:
"Handgun"
,
289
:
"Whiteboard"
,
289
:
"Whiteboard"
,
290
:
"Lizard"
,
290
:
"Lizard"
,
291
:
"Pasta"
,
291
:
"Pasta"
,
292
:
"Snowmobile"
,
292
:
"Snowmobile"
,
293
:
"Light bulb"
,
293
:
"Light bulb"
,
294
:
"Window blind"
,
294
:
"Window blind"
,
295
:
"Muffin"
,
295
:
"Muffin"
,
296
:
"Pretzel"
,
296
:
"Pretzel"
,
297
:
"Computer monitor"
,
297
:
"Computer monitor"
,
298
:
"Horn"
,
298
:
"Horn"
,
299
:
"Furniture"
,
299
:
"Furniture"
,
300
:
"Sandwich"
,
300
:
"Sandwich"
,
301
:
"Fox"
,
301
:
"Fox"
,
302
:
"Convenience store"
,
302
:
"Convenience store"
,
303
:
"Fish"
,
303
:
"Fish"
,
304
:
"Fruit"
,
304
:
"Fruit"
,
305
:
"Earrings"
,
305
:
"Earrings"
,
306
:
"Curtain"
,
306
:
"Curtain"
,
307
:
"Grape"
,
307
:
"Grape"
,
308
:
"Sofa bed"
,
308
:
"Sofa bed"
,
309
:
"Horse"
,
309
:
"Horse"
,
310
:
"Luggage and bags"
,
310
:
"Luggage and bags"
,
311
:
"Desk"
,
311
:
"Desk"
,
312
:
"Crutch"
,
312
:
"Crutch"
,
313
:
"Bicycle helmet"
,
313
:
"Bicycle helmet"
,
314
:
"Tick"
,
314
:
"Tick"
,
315
:
"Airplane"
,
315
:
"Airplane"
,
316
:
"Canary"
,
316
:
"Canary"
,
317
:
"Spatula"
,
317
:
"Spatula"
,
318
:
"Watch"
,
318
:
"Watch"
,
319
:
"Lily"
,
319
:
"Lily"
,
320
:
"Kitchen appliance"
,
320
:
"Kitchen appliance"
,
321
:
"Filing cabinet"
,
321
:
"Filing cabinet"
,
322
:
"Aircraft"
,
322
:
"Aircraft"
,
323
:
"Cake stand"
,
323
:
"Cake stand"
,
324
:
"Candy"
,
324
:
"Candy"
,
325
:
"Sink"
,
325
:
"Sink"
,
326
:
"Mouse"
,
326
:
"Mouse"
,
327
:
"Wine"
,
327
:
"Wine"
,
328
:
"Wheelchair"
,
328
:
"Wheelchair"
,
329
:
"Goldfish"
,
329
:
"Goldfish"
,
330
:
"Refrigerator"
,
330
:
"Refrigerator"
,
331
:
"French fries"
,
331
:
"French fries"
,
332
:
"Drawer"
,
332
:
"Drawer"
,
333
:
"Treadmill"
,
333
:
"Treadmill"
,
334
:
"Picnic basket"
,
334
:
"Picnic basket"
,
335
:
"Dice"
,
335
:
"Dice"
,
336
:
"Cabbage"
,
336
:
"Cabbage"
,
337
:
"Football helmet"
,
337
:
"Football helmet"
,
338
:
"Pig"
,
338
:
"Pig"
,
339
:
"Person"
,
339
:
"Person"
,
340
:
"Shorts"
,
340
:
"Shorts"
,
341
:
"Gondola"
,
341
:
"Gondola"
,
342
:
"Honeycomb"
,
342
:
"Honeycomb"
,
343
:
"Doughnut"
,
343
:
"Doughnut"
,
344
:
"Chest of drawers"
,
344
:
"Chest of drawers"
,
345
:
"Land vehicle"
,
345
:
"Land vehicle"
,
346
:
"Bat"
,
346
:
"Bat"
,
347
:
"Monkey"
,
347
:
"Monkey"
,
348
:
"Dagger"
,
348
:
"Dagger"
,
349
:
"Tableware"
,
349
:
"Tableware"
,
350
:
"Human foot"
,
350
:
"Human foot"
,
351
:
"Mug"
,
351
:
"Mug"
,
352
:
"Alarm clock"
,
352
:
"Alarm clock"
,
353
:
"Pressure cooker"
,
353
:
"Pressure cooker"
,
354
:
"Human hand"
,
354
:
"Human hand"
,
355
:
"Tortoise"
,
355
:
"Tortoise"
,
356
:
"Baseball glove"
,
356
:
"Baseball glove"
,
357
:
"Sword"
,
357
:
"Sword"
,
358
:
"Pear"
,
358
:
"Pear"
,
359
:
"Miniskirt"
,
359
:
"Miniskirt"
,
360
:
"Traffic sign"
,
360
:
"Traffic sign"
,
361
:
"Girl"
,
361
:
"Girl"
,
362
:
"Roller skates"
,
362
:
"Roller skates"
,
363
:
"Dinosaur"
,
363
:
"Dinosaur"
,
364
:
"Porch"
,
364
:
"Porch"
,
365
:
"Human beard"
,
365
:
"Human beard"
,
366
:
"Submarine sandwich"
,
366
:
"Submarine sandwich"
,
367
:
"Screwdriver"
,
367
:
"Screwdriver"
,
368
:
"Strawberry"
,
368
:
"Strawberry"
,
369
:
"Wine glass"
,
369
:
"Wine glass"
,
370
:
"Seafood"
,
370
:
"Seafood"
,
371
:
"Racket"
,
371
:
"Racket"
,
372
:
"Wheel"
,
372
:
"Wheel"
,
373
:
"Sea lion"
,
373
:
"Sea lion"
,
374
:
"Toy"
,
374
:
"Toy"
,
375
:
"Tea"
,
375
:
"Tea"
,
376
:
"Tennis ball"
,
376
:
"Tennis ball"
,
377
:
"Waste container"
,
377
:
"Waste container"
,
378
:
"Mule"
,
378
:
"Mule"
,
379
:
"Cricket ball"
,
379
:
"Cricket ball"
,
380
:
"Pineapple"
,
380
:
"Pineapple"
,
381
:
"Coconut"
,
381
:
"Coconut"
,
382
:
"Doll"
,
382
:
"Doll"
,
383
:
"Coffee table"
,
383
:
"Coffee table"
,
384
:
"Snowman"
,
384
:
"Snowman"
,
385
:
"Lavender"
,
385
:
"Lavender"
,
386
:
"Shrimp"
,
386
:
"Shrimp"
,
387
:
"Maple"
,
387
:
"Maple"
,
388
:
"Cowboy hat"
,
388
:
"Cowboy hat"
,
389
:
"Goggles"
,
389
:
"Goggles"
,
390
:
"Rugby ball"
,
390
:
"Rugby ball"
,
391
:
"Caterpillar"
,
391
:
"Caterpillar"
,
392
:
"Poster"
,
392
:
"Poster"
,
393
:
"Rocket"
,
393
:
"Rocket"
,
394
:
"Organ"
,
394
:
"Organ"
,
395
:
"Saxophone"
,
395
:
"Saxophone"
,
396
:
"Traffic light"
,
396
:
"Traffic light"
,
397
:
"Cocktail"
,
397
:
"Cocktail"
,
398
:
"Plastic bag"
,
398
:
"Plastic bag"
,
399
:
"Squash"
,
399
:
"Squash"
,
400
:
"Mushroom"
,
400
:
"Mushroom"
,
401
:
"Hamburger"
,
401
:
"Hamburger"
,
402
:
"Light switch"
,
402
:
"Light switch"
,
403
:
"Parachute"
,
403
:
"Parachute"
,
404
:
"Teddy bear"
,
404
:
"Teddy bear"
,
405
:
"Winter melon"
,
405
:
"Winter melon"
,
406
:
"Deer"
,
406
:
"Deer"
,
407
:
"Musical keyboard"
,
407
:
"Musical keyboard"
,
408
:
"Plumbing fixture"
,
408
:
"Plumbing fixture"
,
409
:
"Scoreboard"
,
409
:
"Scoreboard"
,
410
:
"Baseball bat"
,
410
:
"Baseball bat"
,
411
:
"Envelope"
,
411
:
"Envelope"
,
412
:
"Adhesive tape"
,
412
:
"Adhesive tape"
,
413
:
"Briefcase"
,
413
:
"Briefcase"
,
414
:
"Paddle"
,
414
:
"Paddle"
,
415
:
"Bow and arrow"
,
415
:
"Bow and arrow"
,
416
:
"Telephone"
,
416
:
"Telephone"
,
417
:
"Sheep"
,
417
:
"Sheep"
,
418
:
"Jacket"
,
418
:
"Jacket"
,
419
:
"Boy"
,
419
:
"Boy"
,
420
:
"Pizza"
,
420
:
"Pizza"
,
421
:
"Otter"
,
421
:
"Otter"
,
422
:
"Office supplies"
,
422
:
"Office supplies"
,
423
:
"Couch"
,
423
:
"Couch"
,
424
:
"Cello"
,
424
:
"Cello"
,
425
:
"Bull"
,
425
:
"Bull"
,
426
:
"Camel"
,
426
:
"Camel"
,
427
:
"Ball"
,
427
:
"Ball"
,
428
:
"Duck"
,
428
:
"Duck"
,
429
:
"Whale"
,
429
:
"Whale"
,
430
:
"Shirt"
,
430
:
"Shirt"
,
431
:
"Tank"
,
431
:
"Tank"
,
432
:
"Motorcycle"
,
432
:
"Motorcycle"
,
433
:
"Accordion"
,
433
:
"Accordion"
,
434
:
"Owl"
,
434
:
"Owl"
,
435
:
"Porcupine"
,
435
:
"Porcupine"
,
436
:
"Sun hat"
,
436
:
"Sun hat"
,
437
:
"Nail"
,
437
:
"Nail"
,
438
:
"Scissors"
,
438
:
"Scissors"
,
439
:
"Swan"
,
439
:
"Swan"
,
440
:
"Lamp"
,
440
:
"Lamp"
,
441
:
"Crown"
,
441
:
"Crown"
,
442
:
"Piano"
,
442
:
"Piano"
,
443
:
"Sculpture"
,
443
:
"Sculpture"
,
444
:
"Cheetah"
,
444
:
"Cheetah"
,
445
:
"Oboe"
,
445
:
"Oboe"
,
446
:
"Tin can"
,
446
:
"Tin can"
,
447
:
"Mango"
,
447
:
"Mango"
,
448
:
"Tripod"
,
448
:
"Tripod"
,
449
:
"Oven"
,
449
:
"Oven"
,
450
:
"Mouse"
,
450
:
"Mouse"
,
451
:
"Barge"
,
451
:
"Barge"
,
452
:
"Coffee"
,
452
:
"Coffee"
,
453
:
"Snowboard"
,
453
:
"Snowboard"
,
454
:
"Common fig"
,
454
:
"Common fig"
,
455
:
"Salad"
,
455
:
"Salad"
,
456
:
"Marine invertebrates"
,
456
:
"Marine invertebrates"
,
457
:
"Umbrella"
,
457
:
"Umbrella"
,
458
:
"Kangaroo"
,
458
:
"Kangaroo"
,
459
:
"Human arm"
,
459
:
"Human arm"
,
460
:
"Measuring cup"
,
460
:
"Measuring cup"
,
461
:
"Snail"
,
461
:
"Snail"
,
462
:
"Loveseat"
,
462
:
"Loveseat"
,
463
:
"Suit"
,
463
:
"Suit"
,
464
:
"Teapot"
,
464
:
"Teapot"
,
465
:
"Bottle"
,
465
:
"Bottle"
,
466
:
"Alpaca"
,
466
:
"Alpaca"
,
467
:
"Kettle"
,
467
:
"Kettle"
,
468
:
"Trousers"
,
468
:
"Trousers"
,
469
:
"Popcorn"
,
469
:
"Popcorn"
,
470
:
"Centipede"
,
470
:
"Centipede"
,
471
:
"Spider"
,
471
:
"Spider"
,
472
:
"Sparrow"
,
472
:
"Sparrow"
,
473
:
"Plate"
,
473
:
"Plate"
,
474
:
"Bagel"
,
474
:
"Bagel"
,
475
:
"Personal care"
,
475
:
"Personal care"
,
476
:
"Apple"
,
476
:
"Apple"
,
477
:
"Brassiere"
,
477
:
"Brassiere"
,
478
:
"Bathroom cabinet"
,
478
:
"Bathroom cabinet"
,
479
:
"studio couch"
,
479
:
"studio couch"
,
480
:
"Computer keyboard"
,
480
:
"Computer keyboard"
,
481
:
"Table tennis racket"
,
481
:
"Table tennis racket"
,
482
:
"Sushi"
,
482
:
"Sushi"
,
483
:
"Cabinetry"
,
483
:
"Cabinetry"
,
484
:
"Street light"
,
484
:
"Street light"
,
485
:
"Towel"
,
485
:
"Towel"
,
486
:
"Nightstand"
,
486
:
"Nightstand"
,
487
:
"Rabbit"
,
487
:
"Rabbit"
,
488
:
"Dolphin"
,
488
:
"Dolphin"
,
489
:
"Dog"
,
489
:
"Dog"
,
490
:
"Jug"
,
490
:
"Jug"
,
491
:
"Wok"
,
491
:
"Wok"
,
492
:
"Fire hydrant"
,
492
:
"Fire hydrant"
,
493
:
"Human eye"
,
493
:
"Human eye"
,
494
:
"Skyscraper"
,
494
:
"Skyscraper"
,
495
:
"Backpack"
,
495
:
"Backpack"
,
496
:
"Potato"
,
496
:
"Potato"
,
497
:
"Paper towel"
,
497
:
"Paper towel"
,
498
:
"Lifejacket"
,
498
:
"Lifejacket"
,
499
:
"Bicycle wheel"
,
499
:
"Bicycle wheel"
,
500
:
"Toilet"
,
500
:
"Toilet"
,
}
}
if
not
with_background
:
if
not
with_background
:
clsid2catid
=
{
k
-
1
:
v
for
k
,
v
in
clsid2catid
.
items
()}
clsid2catid
=
{
k
-
1
:
v
for
k
,
v
in
clsid2catid
.
items
()}
return
clsid2catid
,
catid2name
return
clsid2catid
,
catid2name
\ No newline at end of file
slim/sensitive/sensitive.py
浏览文件 @
1475bb05
...
@@ -35,7 +35,6 @@ set_paddle_flags(
...
@@ -35,7 +35,6 @@ set_paddle_flags(
FLAGS_eager_delete_tensor_gb
=
0
,
# enable GC to save memory
FLAGS_eager_delete_tensor_gb
=
0
,
# enable GC to save memory
)
)
from
paddle
import
fluid
from
paddle
import
fluid
from
ppdet.experimental
import
mixed_precision_context
from
ppdet.experimental
import
mixed_precision_context
from
ppdet.core.workspace
import
load_config
,
merge_config
,
create
from
ppdet.core.workspace
import
load_config
,
merge_config
,
create
...
@@ -85,11 +84,16 @@ def main():
...
@@ -85,11 +84,16 @@ def main():
eval_prog
=
eval_prog
.
clone
(
True
)
eval_prog
=
eval_prog
.
clone
(
True
)
if
FLAGS
.
print_params
:
if
FLAGS
.
print_params
:
print
(
"-------------------------All parameters in current graph----------------------"
)
print
(
"-------------------------All parameters in current graph----------------------"
)
for
block
in
eval_prog
.
blocks
:
for
block
in
eval_prog
.
blocks
:
for
param
in
block
.
all_parameters
():
for
param
in
block
.
all_parameters
():
print
(
"parameter name: {}
\t
shape: {}"
.
format
(
param
.
name
,
param
.
shape
))
print
(
"parameter name: {}
\t
shape: {}"
.
format
(
param
.
name
,
print
(
"------------------------------------------------------------------------------"
)
param
.
shape
))
print
(
"------------------------------------------------------------------------------"
)
return
return
eval_reader
=
create_reader
(
cfg
.
EvalReader
)
eval_reader
=
create_reader
(
cfg
.
EvalReader
)
...
@@ -133,8 +137,8 @@ def main():
...
@@ -133,8 +137,8 @@ def main():
compiled_eval_prog
=
fluid
.
compiler
.
CompiledProgram
(
program
)
compiled_eval_prog
=
fluid
.
compiler
.
CompiledProgram
(
program
)
results
=
eval_run
(
exe
,
compiled_eval_prog
,
eval_loader
,
results
=
eval_run
(
exe
,
compiled_eval_prog
,
eval_loader
,
eval_keys
,
eval_keys
,
eval_values
,
eval_cls
)
eval_values
,
eval_cls
)
resolution
=
None
resolution
=
None
if
'mask'
in
results
[
0
]:
if
'mask'
in
results
[
0
]:
resolution
=
model
.
mask_head
.
resolution
resolution
=
model
.
mask_head
.
resolution
...
@@ -152,12 +156,15 @@ def main():
...
@@ -152,12 +156,15 @@ def main():
pruned_params
=
FLAGS
.
pruned_params
pruned_params
=
FLAGS
.
pruned_params
assert
(
FLAGS
.
pruned_params
is
not
None
),
"FLAGS.pruned_params is empty!!! Please set it by '--pruned_params' option."
assert
(
FLAGS
.
pruned_params
is
not
None
),
"FLAGS.pruned_params is empty!!! Please set it by '--pruned_params' option."
pruned_params
=
FLAGS
.
pruned_params
.
strip
().
split
(
","
)
pruned_params
=
FLAGS
.
pruned_params
.
strip
().
split
(
","
)
logger
.
info
(
"pruned params: {}"
.
format
(
pruned_params
))
logger
.
info
(
"pruned params: {}"
.
format
(
pruned_params
))
pruned_ratios
=
[
float
(
n
)
for
n
in
FLAGS
.
pruned_ratios
.
strip
().
split
(
" "
)]
pruned_ratios
=
[
float
(
n
)
for
n
in
FLAGS
.
pruned_ratios
.
strip
().
split
(
" "
)]
logger
.
info
(
"pruned ratios: {}"
.
format
(
pruned_ratios
))
logger
.
info
(
"pruned ratios: {}"
.
format
(
pruned_ratios
))
sensitivity
(
eval_prog
,
sensitivity
(
eval_prog
,
place
,
place
,
pruned_params
,
pruned_params
,
test
,
test
,
...
@@ -195,7 +202,8 @@ if __name__ == '__main__':
...
@@ -195,7 +202,8 @@ if __name__ == '__main__':
"--pruned_ratios"
,
"--pruned_ratios"
,
default
=
"0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9"
,
default
=
"0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9"
,
type
=
str
,
type
=
str
,
help
=
"The ratios pruned iteratively for each parameter when calculating sensitivities."
)
help
=
"The ratios pruned iteratively for each parameter when calculating sensitivities."
)
parser
.
add_argument
(
parser
.
add_argument
(
"-P"
,
"-P"
,
"--print_params"
,
"--print_params"
,
...
...
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